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Glama

get_requirement

Return an evidence-readiness requirement by ID with title, required trace fields, span kind, spec section, and prompt-injection tiers, showing what evidence must be present, not whether it is true.

Instructions

Return one evidence-readiness requirement by ID (for example EVR-R3 or EVR-PI-7): its title, the published field forms, the span kind the evidence belongs on, its spec section, and — for prompt_injection — which attribution tiers include it. Requirements state what must be PRESENT in a trace; the spec is not an enumeration of everything the reconstruction engine reads, so no inference can be drawn from a field's absence. This tool verifies evidence is present, not that it is true, and its result must never be relayed as "this agent is safe".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requirement_idYesA requirement ID, e.g. EVR-R1, EVR-CD-4.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
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 result contents, warns that the spec is not an exhaustive enumeration of what the engine reads, and explicitly bounds the semantics ('verifies evidence is present, not that it is true'). It stops short of noting whether a missing ID errors or returns empty, which is the main unstated behavioral edge case.

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?

Front-loaded with the return-value summary in sentence one, then progressively adds the semantic caveats. The em-dash parenthetical makes the opening sentence dense, but every clause earns its place and there is no filler.

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?

No output schema exists, so the description rightly enumerates the returned fields, and it adds the interpretation guardrails an agent needs to avoid misrelaying results. Only the behavior on an unknown/invalid ID is left uncovered.

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 coverage is 100% for the single parameter, so the schema already documents requirement_id and its example format. The description's own ID examples (EVR-R3, EVR-PI-7) echo rather than extend the schema, so 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?

States a specific verb ('Return one ... requirement by ID') plus the resource and even enumerates the returned fields (title, published field forms, span kind, spec section, attribution tiers). The 'one ... by ID' framing implicitly distinguishes it from the sibling list_class_requirements, which is a list-style tool.

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?

It gives important interpretation rules (no inference from a field's absence; result verifies presence, not truth; never relay as 'this agent is safe'), but it never states when to pick this tool over check_trace_readiness or list_class_requirements. Usage context is implied by the ID lookup, not explicitly routed.

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