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RankCanon

RankCanon

by codepharm99
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_doc

Fetch the full stored text of a documentation page by URL to let AI agents read complete, cited SEO guidance.

Instructions

Return the full stored text of a documentation page by url.

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": {
      "url": "<url>"
    },
    "name": "get_doc"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYes
max_charsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It says the text is 'full' and 'stored' but does not disclose what happens when max_chars truncates the content, whether the page must already be indexed (corpus_status/search_docs siblings imply a corpus), or any auth/error behavior.

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?

A single front-loaded sentence with no filler. The core action and the key parameter are stated immediately.

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?

For a 2-parameter read tool with no output schema and no annotations, the description covers the return type (full text) but omits truncation semantics and the relationship to the sibling corpus/search tools. It is adequate but leaves real gaps an agent would hit at call time.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 implies url identifies the page, but says nothing about the max_chars parameter, its default of 20000, or whether truncation is silent — half the parameters are effectively 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?

States a specific verb+resource (return the full stored text of a documentation page) and the lookup key (by url). It is clear what the tool fetches, but it never contrasts itself with the sibling search_docs, so an agent must infer the split between search and fetch.

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

Usage Guidelines2/5

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

There is no when-to-use or when-not-to-use guidance and no reference to the sibling search_docs, which is the obvious alternative for discovery. The agent gets no routing signal beyond the tool name.

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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