Tool Search

When you have many MCP servers or non-core plugin tools attached to a session, their JSON schemas can consume a substantial fraction of the context window on every turn — even when only a few of them are relevant to what the user actually asked for.

Tool Search is Hermes' opt-in progressive-disclosure layer for that problem. When activated, MCP and plugin tools are replaced in the model-visible tools array by three bridge tools, and the model loads each specific tool's schema on demand.

Built-in Hermes tools never defer

The tools that make up Hermes' core capability set (terminal, read_file, write_file, patch, search_files, todo, memory, browser_*, web_search, web_extract, clarify, execute_code, delegate_task, session_search, and the rest of _HERMES_CORE_TOOLS) are always loaded directly. Only MCP tools and non-core plugin tools are eligible for deferral.

How it works

When Tool Search activates for a turn, the model sees three new tools in place of the deferred ones:

tool_search(queries, limit?)   search the deferred-tool catalog (one or more queries)
tool_describe(names)           load the full schemas for one or more tools
tool_call(calls)               invoke deferred tools; `calls` is an array of {name, arguments}

calls takes one entry per invocation; a single local call is an array of one. Only connectors__ names may be batched together; mixed and multi-local batches are rejected.

A typical interaction looks like:

Model: tool_search(["create a github issue", "send a slack message"])
  → { results: [ { query: "create a github issue",
                   matches: ["mcp_github_create_issue", ...] },
                 { query: "send a slack message",
                   matches: ["mcp_slack_post_message", ...] } ],
      tools: { mcp_github_create_issue: { description: "...",
                                          required: ["title"], ... },
               mcp_slack_post_message: { ... } } }
Model: tool_describe(["mcp_github_create_issue", "mcp_slack_post_message"])
  → { tools: { mcp_github_create_issue: { parameters: { ... } },
               mcp_slack_post_message: { parameters: { ... } } } }
Model: tool_call({ calls: [{ name: "mcp_github_create_issue",
                             arguments: { title: "...", body: "..." } }] })
  → { ok: true, issue_number: 42 }

Each query in a tool_search call is searched independently against the same catalog (limit applies per query); the per-query groups carry tool names only, while the shared tools map holds each matched tool's description and required parameter names once. Queries are stemmed, so "issues" finds create_issue. Each query group that returns no matches includes an available_sources summary of the connected servers so a lexical miss is not mistaken for a missing capability. tool_describe resolves every requested name in one call; unknown names are reported in not_found without failing the rest of the batch.

When the model invokes tool_call, Hermes unwraps the bridge and dispatches the underlying tool exactly as if the model had called it directly. Pre-tool-call hooks, guardrails, approval prompts, and post-tool-call hooks all run against the real tool name — not against tool_call. The activity feed in the CLI and gateway also unwraps so you see the underlying tool, not the bridge.

When does it activate?

Tool Search uses tiered disclosure: the presence of any deferrable (MCP/plugin) tool activates the bridge; what scales with catalog size is how much of the catalog stays visible, not whether schemas defer.

Tier	Condition	What the model sees
0	No MCP/plugin tools	Every tool eager, no bridge. Pass-through.
1	Deferred catalog's listing fits the budget	Bridge + a skills-style manifest of every deferred tool (name + short description, degrading to names-only when over budget). Degradation is per server: when one oversized server (Cloudflare) is attached alongside small ones (Linear), the small servers keep their per-tool listings and only the oversized server collapses to a summary line.
2	Per-tool listing exceeds the budget even names-only for every server (e.g. Cloudflare's flat API surface alone: ~3,300 tools whose names are ~32K tokens)	Bare bridge + a one-line-per-server summary (server name + tool count), so the model knows which domains are reachable; individual tools are discoverable only through tool_search.

The listing budget is min(threshold_pct% of context, listing_max_tokens). The decision is re-evaluated every time the tools array is built, so adding or removing MCP servers mid-session moves the session between tiers on the next assembly.

Configuration

tools:
  tool_search:
    enabled: auto       # auto (default), on, or off
    threshold_pct: 5    # listing budget as a percentage of context
    search_default_limit: 5
    max_search_limit: 25
    listing: auto       # embed a grouped name+description catalog manifest
    listing_max_tokens: 4000

Key	Default	Meaning
enabled	auto	auto/on activate whenever at least one deferrable tool exists; off disables entirely (everything stays eager). auto is currently an alias of on — it is reserved for a future mode that inlines schemas when they fit the context and defers only when they don't. Pin on or off if you want today's behavior guaranteed across upgrades.
threshold_pct	5	Listing budget as a percentage of the active model's context length. Range 0–100.
search_default_limit	5	Hits returned per query when the model calls tool_search without a limit.
max_search_limit	25	Hard upper bound the model can request via limit (per query). Range 1–50.
listing	auto	Embed a skills-style manifest of every deferred tool (name + first sentence of its description, ≤60 chars, grouped by MCP server) in the tool_search bridge description. auto includes it when it fits the budget (falling back to names-only, then to the tier-2 server summary); on/off force either way.
listing_max_tokens	4000	Absolute cap on the embedded listing, regardless of context size. Range 200–60000. Large catalogs degrade to names-only or per-server summaries, keeping full schemas available through search.

Per-call array caps are internal safety bounds, not configuration. Over-cap calls return an error so the model can retry with a smaller batch.

Why the listing exists

Without it, deferred capabilities are invisible — live benchmarking showed models substituting visible core tools (running gh in the terminal instead of searching for the deferred GitHub tool) or declaring a capability nonexistent instead of calling tool_search. The listing applies the skills pattern to tools: every capability stays discoverable by name at all times, while full parameter schemas remain deferred. If the model sees the exact tool name in the listing, it can skip tool_search and go straight to tool_describe, saving a round trip.

You can also flip the legacy boolean shape:

tools:
  tool_search: true   # equivalent to {enabled: auto}

Connectors (remote tools)

When you are signed in to the Nous Portal, the bridge additionally reaches connectors — remote tools served by the managed tool gateway. They are never registered locally: tool_search sends each query to the gateway, adds the gateway's hits to the local catalog as documents (tagged source: "connectors", named connectors__<connector>__<tool>), and ranks both with the same BM25 pass and the same rarest-token rule, so limit caps the group as a whole and a connector tool that answers the query is never pushed out by local tools that share one word with it. The gateway call is bounded at 30 seconds; a slow or dark gateway degrades to local results only. tool_describe fetches connector schemas from the gateway, and tool_call sends each connector entry in a batch as its own gateway request, in input order (a tool name the gateway does not know under its conventional slug is retried once under the literal slug, so an entry can cost two requests). If a connector ever shipped both GMAIL_X and a literal X, both would compose to connectors__gmail__X, which runs GMAIL_X; search keeps that twin, drops the other, and logs a warning. Results splice back into the batch's original order with recomputed counts.

tools:
  connectors:
    enabled: true   # false — never touch connector routes; the bridge
                    # behaves exactly as if the feature didn't exist

Signed out (or when the gateway does not serve connectors for your account), everything above is invisible: local search behaves exactly as described in the rest of this page, with no errors shown to the model.

A connector call that needs an account you haven't linked returns a CONNECTION_REQUIRED error. The manage_connections tool lists connectors and their connection state and starts an authorization: in the desktop app the call shows a card, blocks until each app is connected or skipped, and reports the outcomes; elsewhere it returns a connect link per app for the user to open. Disconnecting an account is done by the user in the Portal. The same tool also installs, enables and authorizes local MCP servers from the catalog (targets with mcp: true), so it is present whether or not you are signed in; only the managed-connector actions need the sign-in.

tool_call accepts a batch: calls is an array of {name, arguments} entries (a single call is an array of one). Each connector entry in a batch is dispatched as its own gateway request, one after another; local deferred tools stay one entry per tool_call. Approvals settle per entry before dispatch, and a /stop between entries leaves the unstarted ones unsent (their slots report INTERRUPTED).

When NOT to use it

Tool Search trades a fixed per-turn token cost (the three bridge tool schemas plus the catalog listing) and at least one extra round trip on cold tools (describe → call) for the savings on the deferred schemas. At tier 1 the listing keeps every capability visible, so the discovery round trip usually disappears — the model goes straight to tool_describe. Live benchmarking showed the listing mode matching eager loading's task success while costing less than the bare bridge.

If you want the old always-eager behavior for a small toolset, set enabled: off.

Trade-offs that don't go away

These come from the prompt-cache integrity invariant — they are inherent to any progressive-disclosure design, not specific to this implementation:

• One extra round trip on cold tools. The first time the model needs a deferred tool, it spends one or two extra model calls to find and load the schema. The token savings on the static side are real, but a portion is paid back at runtime.
• No cache benefit on deferred schemas. A loaded tool_describe result enters the conversation history (so it does get cached on subsequent turns) but it never benefits from the system-prompt cache prefix.
• No provider-native validation for deferred schemas. tool_describe lets the model read a deferred tool's schema, but the provider still sees only the generic tool_call.arguments object. Hermes therefore coerces and validates the underlying arguments locally before dispatch; the concrete tool or MCP server remains responsible for schemas Hermes cannot safely validate, such as malformed schemas or external references.
• Model-quality dependence. Tool Search assumes the model can write a reasonable search query for the tool it wants. Smaller models do this less well; the published Anthropic numbers (49% → 74% on Opus 4 with vs. without tool search) show the upside but also that ~26 points of accuracy is still retrieval failure.
• Toolset edits invalidate cache. Adding or removing a tool mid- session changes the bridge tools' descriptions (which include the count of deferred tools) and the catalog, so the prompt cache is invalidated. This is the same trade-off as any toolset edit.

Implementation details

• Retrieval: BM25 over tokenized tool name, source name (the MCP server or plugin toolset the tool belongs to, so searching "linear" finds that server's tools even when a tool's own name doesn't carry the service), description, and parameter names, with Snowball stemming (English) applied to both the index and the query so morphological variants match ("issues" finds create_issue). A tool is a result only if it contains the query's rarest token (the one in the fewest tool documents, so the word that names the intent: gmail, github, incident, not send or create). A query whose rarest token appears in no tool returns an empty group with the connected sources and a retry hint, instead of limit tools that share one common word.
• Relevance floor: a tool must match at least half of a query's answerable terms (terms present anywhere in the catalog) before it is offered — sharing one incidental word with a long query is not a match. A hunt for a capability that doesn't exist returns no results instead of a plausible-looking list the model rephrases against forever. The floor only engages from four answerable terms up, so short queries like "list issues" keep full recall, and an exact tool-name query always matches.
• Parallel execution unwraps the bridge. The batch planner decides concurrency on the underlying tool of a tool_call, not on the literal bridge name — so an MCP server opted in via supports_parallel_tool_calls: true keeps its concurrency when its tools are called through the bridge, and tool_search / tool_describe lookups batch concurrently like any read-only tool.
• Catalog is stateless across turns. It rebuilds from the current tool-defs list every assembly — no session-keyed Map. This avoids the class of bug where a stored catalog drifts out of sync with the live tool registry.
• The catalog is scoped to the session's toolsets. tool_search, tool_describe, and tool_call only ever see and invoke tools the session was actually granted. A subagent, kanban worker, or gateway session restricted to a subset of toolsets cannot use the bridge to discover or call a tool outside that subset — the deferred catalog is the deferrable slice of the session's own enabled/disabled toolsets, not the whole process registry.
• No JS sandbox. Hermes uses the simpler "structured tools" mode (search / describe / call as plain functions). The JS-sandbox "code mode" some other implementations offer is a large surface area; we skip it.

See also

• tools/tool_search.py — the implementation
• tests/tools/test_tool_search.py — the regression suite
• The openclaw-tool-search-report PDF in the original implementation PR for the research that shaped the design