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GraphiteAI

graphite-mcp

Official
by GraphiteAI
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

exposure_analysis

Analyze a company's exposure by examining first- and second-degree connections, sector concentration, and dependency risks to identify vulnerabilities.

Instructions

Analyze a company's exposure: 1st and 2nd degree connections, sector concentration, dependency risks. Example: exposure_analysis(entity_id='company:TSM')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_idYesEntity ID to analyze

Implementation Reference

  • graphite_mcp/server.py:110-120 (registration)
    Tool registration for exposure_analysis — defines the tool name, description, and inputSchema within the list_tools() handler. Requires a single 'entity_id' parameter.
    Tool(
    name="exposure_analysis",
    description="Analyze a company's exposure: 1st and 2nd degree connections, sector concentration, dependency risks. Example: exposure_analysis(entity_id='company:TSM')",
    inputSchema={
        "type": "object",
        "properties": {
            "entity_id": {"type": "string", "description": "Entity ID to analyze"},
        },
        "required": ["entity_id"],
    },
),
  • graphite_mcp/server.py:169-172 (handler)
    Handler for exposure_analysis — calls the REST API endpoint /graph/exposure with the entity_id parameter passed as 'entity' query param.
    elif name == "exposure_analysis":
    result = await _get("/graph/exposure", params={
        "entity": arguments["entity_id"],
    })
  • graphite_mcp/server.py:113-119 (schema)
    Input schema for exposure_analysis — a JSON object requiring 'entity_id' (string).
    inputSchema={
    "type": "object",
    "properties": {
        "entity_id": {"type": "string", "description": "Entity ID to analyze"},
    },
    "required": ["entity_id"],
},

Tool Definition Quality

A3.7/5.0
Behavior2/5

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

No annotations exist, so description must fully disclose behavior. It mentions analytical scope but fails to state that it is a read-only operation, or discuss potential performance costs, authorization needs, or what happens on invalid entity IDs. The read-only nature is implied but not explicit.

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?

Extremely concise: two sentences plus an example. Front-loaded with purpose and scope. Every sentence is informative; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers the tool's purpose and key analytical dimensions. However, it omits output format, error handling, and prerequisites (e.g., entity existence). Adequate for a simple analysis but would benefit from more detail on expected results.

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 covers 100% of parameters with basic description. The description adds value by providing an example that reveals the expected format (entity_id='company:TSM'), clarifying the colon-prefixed convention not in the schema. This goes beyond the schema and aids correct invocation.

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?

Description clearly states action 'Analyze' and resource 'company's exposure' with specific aspects (1st/2nd degree connections, sector concentration, dependency risks). Distinguishes from siblings like get_entity and get_relationships which return basic data, while this provides composite analysis.

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?

No explicit when-to-use or when-not-to-use guidance. The description implies utility for exposure analysis but does not contrast with siblings like compare_entities or find_path. Example is helpful but insufficient for 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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