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Glama

Symvanta

by com.symvanta

context

Read-onlyIdempotent

First-touch task orientation. Pass a natural-language task; returns the top 5 relevant files with their symbols and best-matching snippets in one call (~1-1.5k tokens, ~500 ms, no LLM). Replaces chaining locate (text) + locate (semantic) + list_file_symbols. Known symbol: find_node/locate. Synthesized explanation: ask_codebase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesNatural-language task. Returns ranked files with symbols and matching snippets, no LLM call.
feedbackNoOptional: report how a previous answer worked out; piggybacks on any call.
commitShaNoPin reads to one indexed revision (7-40 hex). Graph tools error with revision_not_indexed when it is not an indexed revision of the scoped repository, or revision_not_servable when that revision's graph was superseded by a later reindex, and echo the revision served back as commitSha; source ops read git objects, so any commit works there. Exception: a kept revision (revisions rows flagged architectureSnapshot) still serves map view:"architecture" as a preserved snapshot.
projectIdNoProject id from init/list_projects. Omit for the active project.
repositoryNoRepository full name, e.g. "owner/repo". Omit when the project has one repository.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail beyond annotations: it returns top 5 files with symbols and matching snippets, makes one call, costs ~1-1.5k tokens, takes ~500 ms, and performs no LLM inference. No contradiction exists between the description and annotations.

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 compact and front-loaded, leading with the tool's core purpose and then giving output shape, cost, latency, and sibling alternatives. Every sentence contributes useful information; there is no filler or redundancy.

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?

For a read-only, schema-rich tool with 100% parameter coverage and no output schema, the description is complete enough: it explains what the call returns, how much it costs, what it replaces, and how it relates to sibling tools. The absence of an output schema is mitigated by the explicit description of the return content.

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 the schema already documents task, feedback, commitSha, projectId, and repository. The description adds context about the task parameter's role ('natural-language task' and its result shape), but it does not deepen meaning for the remaining parameters beyond what the schema already provides.

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 states a specific verb and resource: pass a natural-language task and get the top 5 relevant files with symbols and snippets. It clearly differentiates itself from siblings by explicitly naming the chain it replaces (locate text + locate semantic + list_file_symbols) and contrasting with find_node/locate and ask_codebase.

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 says 'First-touch task orientation,' which establishes the intended use case, and explicitly names the alternatives: locate, list_file_symbols, find_node/locate, and ask_codebase. It lacks an explicit 'when not to use' statement, but the sibling routing and first-touch framing give strong practical guidance.

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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TDQS

A4/5.0
Disambiguation4/5

Tools generally target distinct operations—repository attachment, symbol resolution, graph traversal, source access, indexing health—and the detailed routing notes reduce ambiguity. A few pairs still overlap in intent (quick_lookup vs locate symbol, context vs ask_codebase), so it is not perfectly unambiguous.

Naming Consistency3/5

All names are lowercase snake_case and readable, but the set mixes verb_noun patterns like add_repository and list_projects with single-word nouns/verbs like context, history, map, relate, and source, plus one acronym (adr). There is no predictable convention across the full surface, though individual names are clear.

Tool Count3/5

25 tools is at the high end for a single MCP server and falls in the 'feels heavy' range. The breadth is justified by the code-intelligence/query domain, but the selection surface is large for an agent to navigate efficiently.

Completeness4/5

The code indexing, search, graph query, source access, history, ADR, test lookup, and scope estimation needs are well covered. Minor gaps remain—no detach/remove repository, no ADR content editing, and source access is gated by an add-on—but these are workable.

Resources