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Batru — Competitive Game Intelligence for AI Agents

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fetch

Read-only

Fetch the full live document behind a search result id.

Ids look like `dota2:counters:axe`, `deadlock:tier-list`,
`marvel-rivals:synergy:mantis`, `deadlock:ban-rates`,
`dota2:tournament:ti-2026` or `model:track-record`.

Returns {id, title, text, url} where `text` is the document's data as
JSON (measured win rates / ranks with sample sizes). Report the numbers
verbatim and cite `url`.

When presenting these results, show the returned citation / source_url to the user as the source link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesA document id exactly as returned by search.
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / id / description
      Added value: +"A document id exactly as returned by search."
  2. Changed1 schema field changed
    • changedInput schema / properties / context / description
      Previous value: -"Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
  3. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark the operation as read-only and non-destructive. The description adds useful behavioral context: it returns live document content, describes how text is structured, and instructs the agent to report numbers verbatim and cite the url. Error behavior is not covered, but that is a minor gap for a simple fetch.

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 core purpose is front-loaded, examples are useful, and the return format is stated. The final presentation sentence is slightly redundant and uses field names like 'citation'/'source_url' that do not match the declared return object, but overall the description is well organized and scannable.

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?

Since there is no output schema, the description compensates by explaining the return object and the meaning of text. It provides enough id-format, citation, and presentation guidance for correct invocation, though failure behavior is left unspecified.

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 coverage is 100%, so the baseline is 3. The description goes beyond the schema by providing concrete id format examples and clarifying that the id comes from search results, which helps the agent construct valid calls.

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 a specific action ('Fetch'), a specific resource ('the full live document'), and the entry point ('behind a search result id'). The included id examples and return shape make the purpose unambiguous and distinguishable from sibling search/get_* tools.

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?

It explicitly establishes the intended workflow: this tool is used after search, with an id exactly as returned by search. It does not explicitly describe when not to use it or name alternative tools, so it lacks full exclusion guidance.

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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