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trip2g

by io.github.trip2g
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Search notes by query. Returns snippets with a heading breadcrumb (title > section > subsection) that locates the approximate section, plus a precise toc_path per match. Each result carries note_path (string) and note_id (integer); each match carries match_id (string, form "p:c"). Drill-down workflow: 1) search to find the approximate section via the breadcrumb; 2) call note_html(path=<result.note_path>, toc_path=[...]) to read the matched section, or expand(path=<result.note_path>, toc_path=[...]) to navigate the note's structure level by level; 3) note_html(path=<result.note_path>, match_id=<match.match_id>) for a focused chunk window. Each match also carries section_url — a link straight to that heading, for citing the section rather than the whole note. The optional fields each run an extra search whose results are merged with the main one; fill them to find more.

Input Schema

NameRequiredDescriptionDefault
limitNoMax number of results to return (default 6)
queryYesThe user's question exactly as they asked it. Do not rewrite it.
short_queryNoOptional. 2-3 words in English that the answering note is sure to contain: the main term and one qualifier, in dictionary form (nominative singular, infinitive). A short phrase, not a list; no punctuation. Keyword search needs every word to match, so fewer precise words beat many.
detail_limitNoHow many results include full snippet matches; results beyond this are returned as lightweight previews (title, path, score) to save context (default 3)
expected_answerNoOptional. One or two sentences in English that the answering note itself could contain, written the way the notes state facts (e.g. 'To reset a password, open Settings and select Security.'). Not a description of what the note covers, not a question. If unsure of details, stay general but keep it a statement of fact.
rephrased_queryNoOptional. The question asked another way in English: different words, and the canonical name of what it asks about if you know it. One sentence.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / expected_answer
      Added value: +{
+  "description": "Optional. One or two sentences in English that the answering note itself could contain, written the way the notes state facts (e.g. 'To reset a password, open Settings and select Security.'). Not a description of what the note covers, not a question. If unsure of details, stay general but keep it a statement of fact.",
+  "type": "string"
+}
    • changedInput schema / properties / query / description
      Previous value: -"Search query"New value: +"The user's question exactly as they asked it. Do not rewrite it."
    • addedInput schema / properties / rephrased_query
      Added value: +{
+  "description": "Optional. The question asked another way in English: different words, and the canonical name of what it asks about if you know it. One sentence.",
+  "type": "string"
+}
    • addedInput schema / properties / short_query
      Added value: +{
+  "description": "Optional. 2-3 words in English that the answering note is sure to contain: the main term and one qualifier, in dictionary form (nominative singular, infinitive). A short phrase, not a list; no punctuation. Keyword search needs every word to match, so fewer precise words beat many.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and mostly succeeds: it discloses that optional fields each trigger an additional merged search, that detail_limit degrades extra results to lightweight previews to save context, and what each result/match actually contains. It stops short of stating latency/cost implications of the merged searches or result ordering/scoring semantics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and the drill-down workflow are front-loaded and the numbered steps are easy to scan. Some space is spent restating schema types ("note_path (string) and note_id (integer)") that the agent already has, which is mild redundancy but not enough to bloat it.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description correctly spends its effort describing the return shape (breadcrumb, toc_path, match_id, section_url) and how to consume it, which is what an agent needs here. Gaps remain around ordering, pagination, and behavior when zero results match.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is already 100%, so a 3 is the baseline, but the description adds real semantics on top: the optional fields are not simple filters but each run a separate merged search, which changes how an agent should think about supplying them. It does not clarify limit vs detail_limit interaction beyond restating defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence gives a specific verb and resource ("Search notes by query") and immediately distinguishes itself from siblings by naming note_html and expand as the downstream tools a search result feeds into. An agent can tell what this does and where it sits in the family without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lays out an explicit three-step drill-down workflow (search → note_html/expand → note_html with match_id) and tells the agent to fill the optional query fields to widen the net. It never says when to prefer this over similar or federated_search, so the sibling-selection guidance is incomplete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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