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Search agentic-os methodology

search_methodology
Read-onlyIdempotent

Search agentic-os governance, SDLC pipeline, and QE blueprint documentation using full-text queries. Every term must appear in a document for a match; use distinctive words for better results.

Instructions

Full-text search over the agentic-os governance, agentic-sdlc pipeline, and agentic-qe blueprint documentation. Use it first whenever you do not already hold a document URI, then pass a result uri to get_document for the full text. Matching is AND, not OR — every term must appear — and terms match at word starts, so "gate" finds "gates" and "gating" but "ate" finds neither. It searches markdown only: role presets are JSON and never appear here, so use list_presets for those. Read-only and idempotent; searches the server's own bundled corpus, never the network or your repository.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results, best-scoring first (1-25).
queryYesSearch terms, e.g. "write scope enforcement". Every term must appear in a document for it to match, so two or three distinctive words find more than a full sentence does. Runs of fewer than two alphanumeric characters are ignored, and punctuation is not searchable.
pluginNoRestrict results to one plugin: agentic-os (governance), agentic-sdlc (the SDLC pipeline), or agentic-qe (QE blueprints). Omit to search all three, which is usually right unless you already know the area.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesMatching documents, best first. Empty when nothing matched — a normal result, not an error; broaden the query or drop the plugin filter.
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds valuable context: it searches only markdown (not JSON), only the server's bundled corpus, and explains matching in detail. However, it doesn't mention potential issues like rate limits or performance, but given annotations cover safety, the added context is significant.

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

Conciseness5/5

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

The description is concise, using short sentences. It front-loads purpose and usage, then adds behavioral details. No fluff; every sentence earns its place.

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

Completeness5/5

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

Given rich annotations, full schema descriptions, and an output schema (context signal), the description covers scope, behavior, limitations, and sibling tool differentiation. It is sufficiently complete for an AI agent to use correctly.

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 100% with descriptions. The description adds beyond schema: explains AND matching for query, clarifies that omitting plugin searches all three and is usually right, and gives examples (e.g., 'gate' finds 'gates'). This enriches understanding without redundancy.

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 description clearly states the tool does full-text search over specific documentation (agentic-os, agentic-sdlc, agentic-qe). It uses a specific verb+resource and distinguishes from sibling tools by naming get_document and list_presets, making it unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit guidance: use it first when you don't have a document URI, then pass result to get_document. It also explains when not to use (for JSON presets, use list_presets) and describes matching behavior (AND, word-starts), setting correct expectations.

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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