gapup-mcp

bias_amplification_tracker

Read-onlyIdempotent

Tracks bias amplification in LLM outputs by analyzing fairness metrics from HuggingFace's model leaderboard. Designed for risk assessment personas to detect and quantify demographic, gender, or racial bias amplification in generated text. Accepts model identifiers or output samples, returns structured bias metrics and amplification trends.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`modelId`	No	HuggingFace model identifier (e.g., 'facebook/opt-1.3b')
`outputSamples`	No	Array of LLM output strings to analyze for bias amplification
`demographicGroups`	No	Specific demographic groups to monitor (e.g., ['gender', 'race'])

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`biasMetrics`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, so no destructive actions. The description adds that it accepts model identifiers or output samples and returns structured bias metrics and amplification trends, aligning with annotations and providing useful context beyond them.

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 two sentences with no fluff: first sentence defines purpose and data source, second sentence specifies users and outputs. Every part is necessary and efficient.

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?

Given the tool's 4 parameters and presence of an output schema, the description covers purpose, inputs, outputs, and target audience. It lacks detail on specific metrics computed, but the output schema likely provides that, so it is reasonably complete.

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?

Schema description coverage is 100%, so the schema already explains all 4 parameters well. The description adds context by summarizing accepted inputs (modelId, outputSamples, demographicGroups) but does not significantly enhance understanding beyond the schema.

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 the tool tracks bias amplification in LLM outputs using HuggingFace model leaderboard, with a specific verb and resource. It distinguishes itself from sibling tools like model_safety_certification_checker or hallucination_confidence_meter by focusing on fairness metrics and demographic bias.

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 targets 'risk assessment personas' but does not explicitly state when to use this tool over alternatives or provide exclusions. Usage is implied by its focus on bias amplification, but no direct comparison with sibling tools is given.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

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