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search_abstractions

Search the abstraction index by keyword to find compiler abstractions and pass pipelines; results are ranked by relevance and grouped by layer.

Instructions

Search the abstraction index by keyword. Results are ranked by relevance and grouped by layer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
layerNoOptional card layer or framework prefix, e.g. "ptoas", "simpler", "pypto/passes". Results stay grouped by layer.
queryYesKeyword to search across abstraction names, layers, kinds, tags, and related fields
fieldsNo"summary" returns name+layer+one_liner; "full" adds tags, arch_families, repossummary
max_resultsNoMaximum results to return (1–100)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.3.2
    • addedInput schema / properties / layer
      Added value: +{
+  "default": "",
+  "description": "Optional card layer or framework prefix, e.g. \"ptoas\", \"simpler\", \"pypto/passes\". Results stay grouped by layer.",
+  "title": "Layer",
+  "type": "string"
+}
  2. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose two useful traits — results are ranked by relevance and grouped by layer — which go beyond the schema. However, it says nothing about read-only safety, pagination limits, or what happens with an empty query, so gaps remain.

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?

Two short, front-loaded sentences with no filler; the core action comes first and the ranking/grouping behavior second. Slightly terse given four parameters, but nothing is wasted.

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?

An output schema exists, so return-value explanation is properly omitted. What is missing for a search tool is any indication of scope (what the abstraction index contains) and how it relates to explain_abstraction or search_code, making the definition adequate but not fully self-locating.

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 documents query, layer, fields, and max_results with examples and defaults. The description's mention of grouping by layer lightly reinforces the layer parameter but adds no new syntax or format detail. 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?

The description gives a specific verb+resource ("Search the abstraction index by keyword") and states the result ordering, so an agent knows exactly what the tool does. It does not, however, distinguish itself from overlapping siblings such as search_code, search_tracker, or explain_abstraction, leaving the agent to guess which search surface applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use, when-not-to-use, or named alternative. The keyword-search phrasing only weakly implies usage context, and with several sibling search tools and explain_abstraction available, the description provides no routing signal.

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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