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standards-mcp

standards-mcp

by lachinovedhem
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Search standards

search_standards

Find engineering standards by natural-language query, returning rule sections with source and snippet. Use before answering architecture, API, security, or policy questions to apply authoritative rules.

Instructions

Search the standards library with natural language and return the most relevant rule sections (which standard, which heading, and a snippet). Use this before answering any question about architecture, security, API, gRPC, compression, validation, options, messaging, UI/UX, or MCP policy — apply the rules found here rather than from memory.

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": {
      "query": "<query>"
    },
    "name": "search_standards"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax sections to return
queryYesWhat you want the rules for, e.g. 'dapper aot oracle' or 'response compression breach'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It transparently states this is a natural-language search that returns rule sections with standard, heading, and snippet, and implies a read-only operation. It doesn't cover edge cases like no matches or ranking details, but the core behavior is clearly disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence is tight and front-loaded with the core purpose. The second sentence is longer but earns its place by giving an explicit list of topics where the tool should be applied. No filler or redundant phrases are present, though the topic list makes it slightly longer than minimal.

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?

For a simple two-parameter search tool with no output schema, the description adequately covers the query type, return structure, and usage context. It does not mention what happens when no relevant sections are found or how relevance is ranked, but the described output shape is sufficient for an agent to invoke the tool and interpret results.

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% and both parameters are fully documented with descriptions, defaults, constraints, and examples. The description adds no extra parameter-level meaning beyond the natural-language framing of 'query', so the schema already does the heavy lifting and a baseline 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 states a specific verb ('Search'), resource ('standards library'), and output ('most relevant rule sections' with standard, heading, snippet). This clearly distinguishes it from sibling tools list_standards, get_standard, and get_checklist, which imply enumeration or retrieval of specific items.

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 gives explicit context for when to use the tool: before answering questions about architecture, security, API, gRPC, compression, validation, messaging, UI/UX, or MCP policy. It does not explicitly mention sibling alternatives or when not to use it, but the provided topic list and 'instead of from memory' directive create a clear invocation condition.

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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