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73882

feature-separate-batch-eval-mcp

by 73882

feature_separate_batch_eval_insights

Retrieves persisted batch-only evaluation insights for cross-patent analysis. Filters by cohort ID to surface language, domain, and input-structure issues offline.

Instructions

List persisted batch-only insights; never uses network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cohort_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose one meaningful trait: 'never uses network', implying a local, safe read. However it says nothing about permissions, whether results are cached/stale, or idempotency, so it only partially covers the behavioral burden.

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?

A single front-loaded sentence with zero filler; the key resource comes first and the offline trait follows. It is efficient, though the extreme terseness leaves little room for the missing usage and parameter context.

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?

An output schema exists, so return values need not be explained. But for a tool with an undocumented optional filter and several closely related siblings, the description omits both usage routing and parameter meaning, leaving the definition only minimally viable.

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% and the single parameter (cohort_id) is not mentioned in the description at all. Since coverage is low, the description was expected to compensate but adds no meaning about what cohort_id filters or that it is optional/nullable.

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?

The description names a specific verb (List) and resource (persisted batch-only insights), which distinguishes it reasonably from a status/check/tag/run sibling. It does not explicitly say how it differs from those siblings, but 'insights' vs 'status'/'run' is a fairly clear resource distinction.

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 guidance on when to call this tool versus its siblings (_status, _check, _run) or what state must exist first (e.g. whether a batch eval must have run). The only contextual note is that it is offline, which is a behavioral trait rather than a when-to-use rule.

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