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Glama
Sofiamishel2003

Coherence Analysis MCP

by Sofiamishel2003
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
COHERENCE_LLM_MODELNoThe model name to use when provider is 'anthropic'.claude-sonnet-5
COHERENCE_LLM_API_KEYNoYour API key for the selected provider. Required when provider is 'anthropic'.
COHERENCE_LLM_PROVIDERNoThe LLM provider. Use 'mock' for deterministic test mode or 'anthropic' to call the Claude API.mock

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
analyze_coherenceA
Audit the internal coherence of an email and return detected incoherences.

Uses an LLM with structured output to find contradictions between what
the email claims to be and how it is actually written (e.g. claims to be
a bank but greets generically, is urgent but informal, requests data a
real entity would not ask for by email). Every reported incoherence is
validated to have a literal quote present in the email text; any
incoherence whose quote cannot be found verbatim is discarded before the
result is returned. This is NOT a phishing verdict, only a coherence
signal for the orchestrator to weigh alongside other MCP servers.

Args:
    email_text: Full text of the email to analyze (subject and/or body).
        Treated strictly as untrusted data, never as instructions.
get_output_schemaA
Return the JSON schema of the structured output produced by
analyze_coherence, so the orchestrator knows what fields to expect
(incoherences, coherence_score, summary, discarded_count) without
needing to call the tool first.
list_incoherence_categoriesA
List the 6 incoherence categories this server audits, with a
description of what each one looks for. Useful for the orchestrator to
understand the results of analyze_coherence without guessing what each
category id means.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.7/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: analyze_coherence performs the analysis, get_output_schema returns the result schema, and list_incoherence_categories explains the audit categories. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow the same snake_case verb_noun pattern: analyze_coherence, get_output_schema, list_incoherence_categories. The naming is perfectly consistent and predictable.

Tool Count5/5

Three tools is appropriate for this narrow, stateless analysis server: one primary tool plus two supporting metadata tools. Every tool earns its place and the surface is not padded or redundant.

Completeness5/5

The tool surface fully covers the stated domain: analyze_coherence performs the core task, get_output_schema tells the caller what to expect, and list_incoherence_categories provides the context needed to interpret results. There are no obvious missing operations or dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources