Agent Output Guard MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| verify_json_schemaB | Validate JSON data from another agent against expected schema. Essential for preventing malformed data propagation in multi-agent workflows. Returns validation status, errors, and confidence score. |
| detect_hallucination_markersA | Scan agent output for common hallucination patterns, uncertainty markers, and fabrication indicators. Critical for multi-agent reliability. Returns detailed analysis and confidence score. |
| validate_data_freshnessB | Check if data from another agent is recent and valid based on timestamps, staleness indicators, and expected update frequencies. Prevents acting on outdated information. |
| cross_reference_checkB | Compare data from multiple agents for consistency and detect discrepancies. Essential for multi-agent coordination. Returns consistency score and detailed comparison. |
| output_consistency_scoreB | Calculate overall consistency score for agent output including internal logic, format consistency, and reliability indicators. Returns comprehensive reliability assessment. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Most tools have distinct purposes—schema validation, hallucination detection, freshness checks, and consistency scoring all target different concerns. However, cross_reference_check and output_consistency_score overlap somewhat in that both produce consistency/reliability assessments, and an agent could struggle to choose between these two when the objective is 'check consistency.'
The naming pattern is mostly consistent, using descriptive verb_noun compounds (verify_json_schema, detect_hallucination_markers, cross_reference_check). However, there's inconsistency in the verb forms: 'verify,' 'detect,' 'validate,' 'cross_reference' (noun-ified verb), and 'output' (pure noun). The naming style is readable but not uniformly patterned.
Five tools is well-scoped for an output-guard server. Each tool addresses a distinct quality dimension (schema, hallucination, freshness, cross-source consistency, overall score), and the count feels right without redundancy or unnecessary proliferation.
The server covers the core guard concerns—schema, hallucination, freshness, and consistency—but it lacks some common guard functions such as an actual sanitation/repair tool to fix or block invalid output, or a tool to enforce output length/format limits. There's no explicit quarantine or rejection workflow creating a dead end where issues are detected but not actionable.