Letter2025/dsh-tool-search

dsh-tool-search

Tool search & slimming for DeepSeek Harness (DSH): Hermes-style progressive disclosure. When your tool catalog gets large (many MCP servers or plugin tools), every tool’s JSON schema is injected into the model context on every turn — wasting tokens on tools the task never uses. This plugin collapses the long tail behind three bridge tools and lets the model discover and load them on demand through a configured rerank model.

• Core tools stay eager — file/shell/essential tools are always directly visible.
• Bridge tools — tool_search, tool_describe, tool_call replace the deferred schemas.
• Tiered disclosure — the visible listing shrinks automatically as the catalog grows.
• Conversational setup — the bundled tool-slimmer-setup skill groups your tools by talking to you, and guides configuring the rerank model.
• User or project config — groups/matcher live in ~/.dsh/dsh-tool-search.json (global) or <workspace>/.dsh/dsh-tool-search.json (per project), your choice.

Install

dsh plugin --profile web add dsh-tool-search

How it works

Inspired by Hermes Agent’s Tool Search. On every turn, the plugin’s system-prompt/assemble listener computes the tier and replaces the model-visible tools:

Budget = min(thresholdPct% × contextWindow, listingMaxTokens), recomputed every turn. The manifest rides a runtime context, so it survives complete-prompt composition.

Dynamic injection: searching, describing, or calling a deferred tool warms it into the session’s visible set (LRU-bounded by maxWarmTools), so its full schema is injected into the context for later turns — the model pulls tools into context on demand instead of keeping everything.

When tool_call runs, the plugin executes the real tool by name through ctx.tools.execute, so approvals, guards, and session events all reference the underlying tool — never the bridge. tool_search ranks with the configured rerank matcher and falls back to keyword matching (exact name > name tokens > description tokens) when no matcher is configured or the rerank call fails.

Setup (conversational)

Ask your agent:

帮我配置 dsh-tool-search 的工具分组

The tool-slimmer-setup skill will read the catalog, propose groups, confirm with you, ask whether the config should be global or per-project, and guide you through configuring the rerank matcher (required for tool_search and preload).

Configuration

Static tuning lives in your profile cordis.patch.yml (restart to change):

- id: tool-search
  config:
    enabled: auto        # auto | on | off
    thresholdPct: 5      # listing budget as % of context window
    listingMaxTokens: 4000
    configScope: auto    # user | project | auto (project file wins when present)
    core: [todo_write]   # extra always-eager tools
    maxWarmTools: 8      # LRU cap for dynamically injected tools

Tool groups, the matcher, and preload live in the runtime file (~/.dsh/dsh-tool-search.json for user scope):

{
  "version": 1,
  "scope": "user",
  "groups": [
    { "name": "git", "tools": ["git_status", "git_diff"] },
    { "name": "mcp-github", "prefixes": ["mcp_github_"] }
  ],
  "matcher": {
    "endpoint": "https://dashscope.aliyuncs.com/compatible-mode/v1/rerank",
    "apiKey": "sk-...",
    "model": "qwen3-reranker",
    "topN": 20
  },
  "preload": { "enabled": false, "topK": 5 },
  "core": ["read_file", "write_file"]
}

• groups: exact tool names and/or name prefixes; a tool belongs to the first matching group.
• matcher: an OpenAI-compatible /v1/rerank endpoint — the only matcher type. tool_search returns setup guidance until one is configured.
• preload: optional; when enabled (with a matcher), the session’s first turn semantically preloads the top-K matching tools into the eager set.
• The file is watched by mtime and hot-reloads; changes take effect on the next turn.

Bridge tools

Design notes & pitfalls

• Slimming only rewrites the model-visible surface (system-prompt/assemble); the registry stays complete, so deferred tools remain executable.
• The bridge, the two setup tools, and skill are always eager and never defer themselves.
• Keyword matching is only a fallback: rerank is the primary ranking; without a matcher or on rerank failure, search degrades to keyword matching (exact name > name tokens > description tokens) and never errors.
• See DESIGN.md for the full architecture.

Links

• GitHub
• npm
• Design doc

License

MIT