Skip to main content
Glama
impact-mcp
by 0xmortuex

symbol_users

Identify files that import, re-export, or access a given symbol from a module to map dependents and assess change impact.

Instructions

Who uses symbol from module? Best effort and labeled as such: from-imports, named imports, attribute access on the module alias (mod.name), wildcard imports, and re-export chains (init.py, all, export {x} from). getattr()/dynamic use is invisible.

Input Schema

NameRequiredDescriptionDefault
limitNo
moduleYes
symbolYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it enumerates exactly which reference forms are detected (from-imports, named imports, module-alias attribute access, wildcard imports, re-export chains) and explicitly discloses the blind spot for getattr()/dynamic use. That is real behavioral context an agent cannot get from the schema. It stops short of describing result ordering or how the limit interacts with truncation.

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?

Two tight sentences, front-loaded with the core question, followed by the coverage caveat. Every clause adds information; nothing is redundant.

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?

An output schema exists, so return-value explanation is unnecessary, and the description covers detection scope and known limitations well. The only meaningful gap is the unexplained `limit` parameter and any note on result ordering or truncation behavior.

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 0%, so the description must compensate. It implicitly clarifies `module` and `symbol` through examples like mod.name and __init__.py, but the `limit` parameter (default 100) is never mentioned, leaving pagination/truncation semantics undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise question form, 'Who uses `symbol` from `module`?', which names both the verb (find users) and the resource (a specific symbol within a module). This is clearly narrower than module-level siblings like dependents or dependencies, though it never names those siblings explicitly to draw the contrast.

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

Usage Guidelines3/5

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

Usage is implied by the question framing and the 'best effort' caveat signals when results may be incomplete, but there is no explicit when-to-use versus when-to-use-a-sibling guidance (e.g., dependents vs symbol_users) and no stated prerequisites.

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