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nikolai-vysotskyi

trace-mcp

by nikolai-vysotskyi
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TypeScript
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find_usages

Read-onlyIdempotent

Find all places that reference a symbol or file: imports, calls, renders, dispatches. Understands semantic relationships, not text matches, so you get accurate usages across code and frameworks.

Instructions

Find all places that reference a symbol or file (imports, calls, renders, dispatches). Use instead of Grep for symbol usages — understands semantic relationships, not just text matches. For bidirectional call graph use get_call_graph instead. By default, weakly-grounded text_matched edges into a target whose simple name collides with many other symbols are dropped (phantom god-node filter). Pass include_ambiguous_text_matched: true to keep them. Read-only. Returns JSON: { references: [{ file, line, kind, context }], total, ambiguous_filtered? }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fqnYesFully qualified name to find references for
file_pathYesFile path to find references for
symbol_idYesSymbol ID to find references for
detail_levelNoOutput verbosity. "minimal" returns ~40-60% fewer tokens (drops scores, fqn, signatures, summaries — keeps name/file/line). Use when you only need to pick a candidate before drilling in with get_symbol. Default: "default".
include_ambiguous_text_matchedNoKeep text_matched edges whose target name collides with >=3 other symbols (default false — they produce phantom god-nodes).

Schema Changelog

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

  1. Changed1 schema field changedv3.3.0
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
  2. Added
  3. Removedv1.38.0
  4. Changed4 schema fields changedv1.35.1
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / detail_level
      Added value: +{
+  "description": "Output verbosity. \"minimal\" returns ~40-60% fewer tokens (drops scores, fqn, signatures, summaries — keeps name/file/line). Use when you only need to pick a candidate before drilling in with get_symbol. Default: \"default\".",
+  "enum": [
+    "minimal",
+    "default",
+    "full"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / include_ambiguous_text_matched
      Added value: +{
+  "description": "Keep text_matched edges whose target name collides with >=3 other symbols (default false — they produce phantom god-nodes).",
+  "type": "boolean"
+}
    • addedInput schema / required
      Added value: +[
+  "symbol_id",
+  "fqn",
+  "file_path"
+]
  5. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent behavior, and the description adds meaningful operational detail: the default dropping of weakly-grounded text_matched edges with the phantom god-node rationale, plus the exact JSON return shape. This goes well beyond what annotations alone provide.

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 yet information-dense, front-loading the core purpose and usage guidance before behavioral details and return format. Every sentence adds operational value; there is no filler or repetition of schema content.

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 complex reference-finding tool with no output schema, the description supplies the return JSON shape, keys, optional ambiguity filter, and clear sibling differentiation. All five parameters are covered in the schema, and the description fills the remaining behavioral gaps needed for correct invocation.

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%, so baseline is 3. The description adds semantic context for include_ambiguous_text_matched ('weakly-grounded', 'phantom god-node filter') beyond what the schema states, and clarifies the intent of the return fields. It does not need to restate every parameter.

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 names a specific verb and resource ('Find all places that reference a symbol or file') and enumerates reference kinds ('imports, calls, renders, dispatches'). It distinguishes itself from sibling tools like Grep and get_call_graph without requiring schema inspection.

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?

It explicitly says to use this tool instead of Grep for semantic symbol usages and directs users to get_call_graph for bidirectional call graphs. It also explains when to pass include_ambiguous_text_matched, leaving no ambiguity about tool selection.

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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