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fernandogarzaaa

chimeralang-mcp

by fernandogarzaaa
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Python
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chimera_deliberate

Multi-perspective deliberation. Default mode 'semantic' uses stance detection + prompt-term alignment + concept overlap — reports consensus_detected:true when >=60% of perspectives share a stance AND avg_similarity>=0.62.

Instructions

Multi-perspective deliberation. Default mode 'semantic' uses stance detection + prompt-term alignment + concept overlap — reports consensus_detected:true when >=60% of perspectives share a stance AND avg_similarity>=0.62. Mode 'lexical_consensus' uses raw Jaccard token overlap (faster, but misses paraphrases — use only when vocabulary is controlled). Vs. direct reasoning: externalises the perspective set so callers can inject viewpoints not all present in one model pass, and provides a numeric divergence score the model cannot compute on its own without a separate summarisation step.

Input Schema

NameRequiredDescriptionDefault
modeNosemantic (default): stance+prompt-term+overlap similarity. lexical_consensus: raw Jaccard token overlap — fast but blind to paraphrases.semantic
promptNo
perspectivesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.4
    • changedInput schema / required
      Previous value: -[
-  "prompt",
-  "perspectives"
-]New value: +[]
  2. First observedv0.8.1

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the exact consensus thresholds (>=60% shared stance AND avg_similarity>=0.62), names an output field (consensus_detected), and states tradeoffs of the lexical mode (faster, blind to paraphrases). It does not cover side effects, permissions, or the full return shape, so it is not exhaustive.

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?

The block is dense but front-loaded with purpose, then mode behavior, then alternatives. Every clause is informative with no filler, though the single-paragraph packing of thresholds and comparisons is slightly hard to scan.

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 moderately complex tool with no output schema and no annotations, the description supplies the algorithm, decision thresholds, mode tradeoffs, and a hint of the return (consensus_detected, divergence score). It is close to complete, missing only the semantics of prompt and perspectives and the broader output structure.

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 only 33%: mode is well-documented in both schema and description, but prompt and the nested perspectives/content/perspective fields have no description anywhere. The description adds real meaning for mode (algorithm and tradeoffs), which partly compensates, but the other two parameters remain unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence states a specific verb+resource ("Multi-perspective deliberation") and the description goes on to specify the two operating modes and their algorithms. It is clear what the tool does, though it does not differentiate itself from the many deliberation/aggregation siblings (e.g. chimera_quantum_vote, chimera_summarize).

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 explicit mode-selection guidance ("use only when vocabulary is controlled" for lexical_consensus) and positions the tool against an alternative workflow ("Vs. direct reasoning"). This covers when-to-use and one when-not, but stops short of routing among the sibling tools.

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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