Skip to main content
Glama
DarkMatterProductions

mcp-project-context-server

search_adr_index

Search architecture decision records in a project using semantic queries. Retrieve relevant ADR paths to review context before implementation.

Instructions

Semantically search only the architecture decision records under .context/decisions/. Use this to find ADRs relevant to your current task, then pass their paths to load_context_files — do not rely on this tool's snippets alone. If you need to search across all files in the project, use search_project_files instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query
n_resultsNo
project_pathYesAbsolute filesystem path, a short 'owner/repo' identifier, or a full https:// repository URL.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesIndividual matching hits, one per matched chunk.
warningNoPresent only when the index was built with a different embedding provider/model.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

The description reveals that the tool returns snippets (not full content) and that a follow-up load_context_files is needed for full ADRs. It also implies a semantic search mechanism. However, it does not explicitly state read-only behavior or output structure, though these are not critical given the search context.

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, consisting of two sentences that efficiently convey purpose, usage, and alternatives. It is front-loaded with the core action and includes necessary caveats without unnecessary detail.

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?

Given the tool's simplicity and the existence of an output schema, the description adequately covers how to invoke it and what to do with results. The note to load full context files is particularly important and included, making the description sufficient for correct usage.

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

Parameters3/5

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

The schema provides descriptions for query and project_path, and the tool description reinforces their purpose. However, n_results is only given a default value without any explanation in either schema or description, leaving its meaning partially unclear to the agent.

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's function: semantically searching architecture decision records within .context/decisions/. It also distinguishes this tool from alternatives like search_project_files and search_context_index, making its scope 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 explicitly instructs when to use this tool (find ADRs for the current task) and when to use an alternative (search all project files via search_project_files). It also advises not to rely solely on snippets and to load full context files, providing complete usage guidance.

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