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HydraGraph MCP

HydraGraph MCP

by sayan365
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explain_context

Finds the most relevant symbol for a codebase question and returns its callers, callees, call-site evidence, and two-hop change impact from HydraDB to answer the question.

Instructions

Given a natural-language question about the codebase, find the most relevant symbol and return its callers, callees, call-site evidence, and two-hop change impact from HydraDB. Use this structured graph data to answer the user's question yourself.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "<question>"
    },
    "name": "explain_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesFree-text codebase question, for example: what happens if I change analyzeWithAI's return type?

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden and does a good job explaining the behavior: it finds the most relevant symbol, retrieves structured graph data, and instructs the agent to use that data to answer the user's question. It implies a read-only analysis operation through verbs like 'find' and 'return', though it doesn't explicitly state 'read-only'.

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 two sentences long, front-loaded with the core workflow, and has no filler or redundant content. Every clause adds meaningful information about inputs, outputs, or how to use the returned data.

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?

Given the absence of an output schema, the description adequately enumerates the key return items (callers, callees, call-site evidence, two-hop impact) and the self-answering instruction. It doesn't mention limitations or edge cases, but for a single-parameter tool with simple inputs, this is reasonably complete.

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?

The schema fully describes the only parameter 'question' with an example, and the description adds no extra semantic detail beyond restating it's a natural-language codebase question. With 100% schema coverage, the baseline score of 3 is appropriate.

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 clearly states the tool's function: take a natural-language question, find the most relevant symbol, and return callers, callees, call-site evidence, and two-hop change impact. This specific output set distinguishes it from sibling tools find_callers and impact_of_change, which are narrower.

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 provides clear context for when to use it: when a natural-language question about the codebase needs a structured graph data answer. It does not explicitly mention sibling alternatives or exclusions, but the usage context is sufficiently clear for an agent to select it.

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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