Skip to main content
Glama
jgstew

bigfix-root-mcp

by jgstew

Analyze Relevance

analyze_relevance

Statically analyze a relevance expression without evaluating it to validate syntax, infer types, classify session or client dialect, and flag unknown inspectors or unbound 'it' before running queries.

Instructions

Statically analyze a relevance expression WITHOUT evaluating it anywhere.

Offline and instant: checks that the expression parses (with a positioned error when it does not), infers types, classifies the dialect (session vs client), flags inspectors no reference dump defines, finds unbound 'it', and scores complexity/evaluation cost. Use it to validate a query before session_relevance_query or client_query, or to understand a relevance error after one.

Advisory: the inspector table is a snapshot, so an unknown inspector is a warning (the live server may still know it), and a clean report does not guarantee the server will accept the query.

Input Schema

NameRequiredDescriptionDefault
dialectNoPin the dialect: 'session' (root server) or 'client' (agent). Default: classify automatically from the text.
platformNoNarrow client inspector lookups to one platform, e.g. 'windows', 'macos', 'ubuntu'.
relevanceYesRelevance expression to analyze. It is never evaluated.
include_sexprNoInclude the parsed tree as an S-expression.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it declares the operation is offline and instant, guarantees the expression is never evaluated, and discloses the advisory nature of the inspector snapshot plus the fact that a clean report does not guarantee server acceptance. These are non-obvious behavioral caveats an agent needs.

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?

Front-loaded with the core purpose, then behavior, then usage routing, then caveats. The bullet-style clauses (parse checks, type inference, dialect classification, unbound 'it', complexity scoring) are dense but each earns its place by telling the agent what the report contains.

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?

An output schema exists, so return values need no explanation, and the description still covers the operational profile and limitations an agent must know to interpret results correctly. Nothing needed to call or correctly trust the tool is missing.

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?

Schema description coverage is 100%, so all four parameters are already documented, and the description does not add syntax or format detail beyond them. It reinforces concepts like dialect classification and inspector lookups, but that information is already present in the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('Statically analyze a relevance expression') and immediately scopes it with 'WITHOUT evaluating it anywhere', which is the exact distinction that separates it from session_relevance_query and client_query. An agent can identify the tool's role without opening the schema.

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?

Explicitly names when to use it ('to validate a query before session_relevance_query or client_query, or to understand a relevance error after one'), naming the exact sibling tools and the pre/post conditions. Nothing is left to inference.

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