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fernandogarzaaa

chimeralang-mcp

by fernandogarzaaa
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Python
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chimera_score

Score messages 0–1 for context-window management. mode='drop_priority' (default): scores by recency+type+density — lowest scores are dropped first in lossy compression.

Instructions

Score messages 0–1 for context-window management. mode='drop_priority' (default): scores by recency+type+density — lowest scores are dropped first in lossy compression. mode='importance_for_goal': scores by alignment with the focus goal — highest scores are most relevant to keep. Vs. direct reasoning: O(n) tokenisation is far cheaper than asking the model to rank N messages, which consumes O(N*content) prompt tokens.

Input Schema

NameRequiredDescriptionDefault
modeNodrop_priority: score for compression — lowest scores evicted first. importance_for_goal: score by focus alignment — highest scores most relevant.drop_priority
focusNoTask focus/query. Required for importance_for_goal mode; improves drop_priority scoring.
messagesYesMessages to score [{role, content}]

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.4
    • addedInput schema / properties / messages / items
      Added value: +{
+  "properties": {
+    "content": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "array"
+        }
+      ],
+      "description": "Message text, or a list of content blocks ({type: text, text})."
+    },
+    "role": {
+      "description": "user | assistant | system",
+      "type": "string"
+    }
+  },
+  "required": [
+    "content"
+  ],
+  "type": "object"
+}
  2. First observedv0.8.1

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries full behavioral burden and does disclose meaningful traits: the directional semantics of each mode and the O(n)-vs-O(N*content) cost tradeoff versus direct model reasoning. It still omits whether the operation is read-only/pure, its determinism, and the shape of its return value.

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?

Purpose and output range are front-loaded in the first sentence, followed by mode semantics. It is a single dense but efficient block; the closing cost-comparison sentence is context rather than filler, though slightly promotional.

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

Completeness3/5

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

For a 3-param tool with an enum mode, no annotations, and no output schema, the description covers scoring semantics and cost but never explains the return structure (e.g., whether scores map positionally to input messages). That return-shape gap matters precisely because no output schema exists to cover 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 coverage is 100%, so the schema already documents mode, focus, and messages including the mode enum and the focus requirement. The description restates the mode semantics already present in the schema, adding little value beyond the structured fields, so baseline 3 applies.

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?

States a specific verb (score) and resource (messages) plus the output range (0–1) and the domain (context-window management). It clearly explains the two scoring modes, but never names or differentiates itself from sibling tools like chimera_compress or chimera_summarize, which is the remaining gap for a 5.

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?

It gives clear guidance on which mode to choose and what each mode's scores mean operationally (lowest dropped first vs. highest kept). However, it offers no guidance on when to reach for this tool versus sibling compression/summarization tools, so usage is only implied at the tool-selection level.

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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