Skip to main content
Glama

Marker Parity (source vs rendered)

lens_consistency_markers

Check that evidence markers in a source artifact appear in every rendered output, catching dropped caveats, citations, or hedges between formats. Deterministic verification with exit 6 on violation.

Instructions

Check that evidence markers counted in a source artifact survive into every rendered output. Deterministic, no LLM, exit 6 on a violation. Catches the caveat, citation or hedge that gets dropped between formats — the source says 'as-of 2026-04' and the rendered deck quietly does not. Matching is CASE-SENSITIVE (unlike lens_consistency_leaks, which is case-insensitive), so pick markers whose casing is stable across source and render: 'TRIPWIRE' in the source will NOT match 'Tripwire' in the render and will read as dropped when nothing was. TRIPWIRE: a deliberate subset render also under-counts legitimately. Before treating a hit as a removed caveat, grep the rendered file case-insensitively.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoWorking directory to run in. catches.jsonl is read from and written to here. Defaults to the server's cwd.
sourceYesThe source artifact (authoritative marker counts)
markersNoExplicit marker strings (otherwise taken from the profile)
profileNoProfile YAML providing the marker set
renderedYesRendered output file(s) to check against the source

Schema Changelog

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

  1. First observedv1.1.1

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral burden and meets it: deterministic, no LLM, exit 6 on violation, case-sensitive matching, and two false-positive traps (case mismatch and deliberate subset rendering). The concrete TRIPWIRE example makes the matching behavior unambiguous.

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?

Core purpose is front-loaded and the paragraph stays dense with useful caveats. The 'TRIPWIRE:' sentence is cryptic and slightly interrupts the flow, preventing a 5.

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?

For a 5-parameter tool with no annotations or output schema, it covers the main behavior, exit status, matching mode, and practical verification steps. It does not state the success exit condition or the full return/result shape, and it leaves profile/marker interplay to the schema, but the description is otherwise complete enough to invoke correctly.

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 covers all 5 parameters, so baseline is 3. The description adds value beyond the schema by instructing users to choose markers with stable casing and by warning that a deliberate subset render legitimately under-counts, which directly informs how markers, source, and rendered should be used.

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 first sentence names a specific verb and resource: check that source artifact markers survive into every rendered output. The title and examples further establish it as a parity/consistency verifier, and the explicit case-sensitivity contrast with lens_consistency_leaks sets it apart from siblings.

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 gives clear operational context: use markers with stable casing, and verify doubtful hits case-insensitively before reporting. It names lens_consistency_leaks as the case-insensitive alternative, though it never explicitly says 'use this tool when...' or lists exclusion conditions.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mrhpython/lens-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server