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freshness_check

Run freshness watchdog. Expires stale evidence, downgrades GREEN->YELLOW->GREY if evidence too old.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_idNoEntity ID (optional, checks all)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the main behavioral effect (expiring stale evidence and downgrading colors) and the condition (evidence too old), but it does not clarify whether changes are permanent, reversible, or what 'too old' means. The mutation effect is clear but lacks additional safety or side-effect context.

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?

The description is extremely concise—two short sentences—and immediately states the core purpose. No filler or redundant information; it earns every word.

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?

The tool is simple (one optional param), and the schema covers the parameter. However, there is no output schema and no annotation, so the description should at least convey what the tool returns (e.g., a list of affected IDs or new statuses). It also doesn't mention the overall scope when entity_id is omitted, though the schema hints at 'checks all'. Overall it's adequate for a simple watchdog but leaves some ambiguity about the result.

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?

The schema coverage is 100% with a clear description for entity_id ('optional, checks all'). The tool description adds no parameter-specific detail beyond this, so the baseline of 3 is appropriate since the schema does the heavy lifting.

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 verb ('Run') and resource ('freshness watchdog'), and details the exact action: expires stale evidence and downgrades statuses GREEN->YELLOW->GREY. This is sufficiently distinct from sibling tools like 'whatif_stale' or 'health_check'.

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 description implies usage (run the watchdog to enforce freshness and downgrade old evidence) but provides no explicit when-to-use or alternative tool guidance. It doesn't mention prerequisites, frequency, or when not to use it.

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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TDQS

B3.1/5.0
Disambiguation3/5

Several tools overlap in purpose, particularly contract analysis (check_contract, contract_analyze, llm_clause_check) which could confuse agents. Assessment-related tools (readiness_check, assess_all, article_status) also have partially overlapping functionality. However, most tools have distinct resources and actions with detailed descriptions.

Naming Consistency4/5

Tool names are consistently snake_case and mostly follow a verb_noun pattern (e.g., create_entity, generate_report, register_provider). Minor deviations like contract_analyze and llm_clause_check invert the verb-noun order, but the overall pattern is predictable.

Tool Count2/5

50 tools is a very large surface for a single server, exceeding the threshold for 'too many' tools. While the DORA domain is broad, this breadth makes it challenging for agents to navigate and select the right tool efficiently.

Completeness4/5

The tool set covers the full DORA compliance lifecycle: entity onboarding, contract analysis, provider management, assessments, evidence, reporting, and incident workflows. Simulation and cross-regulation tools add depth. Minor missing CRUD operations (e.g., update/delete entity) but agents can work around.

Resources