Skip to main content
Glama

mort_docs

Search the Mort language specification and reference docs to find best-matching sections for syntax, standard library signatures, and compiler examples.

Instructions

Search the Mort language specification and reference docs.

Returns the best-matching sections, e.g. query="resource struct cleanup", "integer overflow", "slices", "error propagation ?". Requires MORT_HOME (a Mort checkout) since pip installs don't ship the docs.

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": "mort_docs"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the return shape (best-matching sections) and an important environmental prerequisite (MORT_HOME, a Mort checkout, since pip installs omit the docs). It does not say what happens when MORT_HOME is unset or when no section matches, which is the remaining gap.

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?

Three compact lines with zero waste: purpose first, return behavior second, prerequisite third, with the examples embedded where they add value. Every sentence earns its place and nothing is buried.

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?

This is a simple two-parameter read tool with an output schema, so return values need not be explained. The definition covers purpose, environment setup, and query shape; only `limit` semantics and failure behavior are missing, which are modest omissions at this complexity.

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 0%, so the description must compensate. It adds real meaning for `query` by showing four representative search phrases, but `limit` (default 3) is never mentioned or explained—only indirectly implied by "best-matching sections." Half the parameter surface is left to inference.

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 and resource: "Search the Mort language specification and reference docs," which is clearly distinct from sibling operations like mort_run or mort_format. It never explicitly contrasts itself with a sibling such as mort_std, so it lands just short of a full 5.

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?

The example queries ("resource struct cleanup", "integer overflow", "slices", "error propagation ?") imply the intended use case of looking up language semantics, but there is no explicit when-to-use guidance, no exclusions, and no named alternative. Usage must be inferred from the examples rather than being stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.