gapup-mcp

hallucination_confidence_meter

Read-onlyIdempotent

Evaluates the likelihood of hallucination in LLM responses by comparing against HuggingFace model confidence scores. Designed for risk assessment personas to quantify response reliability. Accepts text snippets or model outputs, returns confidence metrics and potential hallucination warnings. Cross-references with top-performing models from the HuggingFace leaderboard.

Input Schema

TableJSON Schema

Name	Required	Description
`text`	Yes	The LLM-generated text to evaluate for hallucination risk
`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.
`model_id`	No	Optional specific HuggingFace model ID to use for evaluation
`threshold`	No	Confidence threshold below which hallucination warnings are triggered

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`confidence_scores`	No
`hallucination_likelihood`	No

TDQS

A4.1/5.0

Behavior4/5

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

The description adds behavioral context beyond annotations: it accepts text snippets or model outputs, returns confidence metrics and potential hallucination warnings, and cross-references with top HuggingFace models. It does not contradict any annotations (readOnlyHint, openWorldHint, idempotentHint). No mention of rate limits or auth needs, but these are not expected from annotations either.

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 extremely concise: three sentences that efficiently convey the purpose, target users, inputs, and outputs. Every sentence adds value with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, output schema exists, annotations provided), the description covers all necessary information: what it does, for whom, what it takes, and what it returns. No gaps are apparent, and it is complete for an agent to decide whether to use it.

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% for all 4 parameters, so the baseline is 3. The description does not add new meaning beyond what the schema already provides for each parameter (text, async, model_id, threshold). It mentions 'text snippets or model outputs' which aligns with the text parameter but adds no new semantics.

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 that the tool evaluates hallucination likelihood in LLM responses by comparing against HuggingFace model confidence scores. It specifies the target audience (risk assessment personas) and the output (confidence metrics and warnings). This distinguishes it well from sibling tools.

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 mentions it is designed for risk assessment personas, giving a clear context of use. However, it does not explicitly state when not to use this tool or provide alternatives among the many sibling tools, such as bias_amplification_tracker or jailbreak_attempt_detector.

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.

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