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by LinkedParticles
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Python
Hybrid

subjects_search

Find subjects by case-insensitive substring across canonical names and aliases, with limit and offset for stable pagination.

Instructions

Substring search over subject names and aliases (case-insensitive).

Args:
    query: Substring to match against ``canonical_name`` or any recorded
        alias, so "speaker" finds a subject whose alias is "the speaker".
    limit: Maximum subjects to return (default 50). A generic
        substring like "the" can match thousands of subjects, so
        this caps the page size.
    offset: Number of matches to skip before returning results
        (default 0). Results are alphabetical by canonical_name
        so pagination is stable.

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes
offsetNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.210.2

TDQS

A4/5.0
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 disclose matching semantics (canonical_name or any alias, case-insensitive), result ordering (alphabetical by canonical_name), and that pagination is stable. It doesn't disclose error or empty-result behavior, which keeps it short of a 5.

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?

One-line purpose is front-loaded before the Args block, and each parameter sentence adds real information. The docstring-style 'Args:' formatting is slightly less scannable than prose but nothing is wasted.

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?

Output schema exists so return values needn't be explained; matching, ordering, and pagination are all covered. The remaining gap is guidance on choosing this over subjects_list/subjects_show, which matters given the crowded sibling set.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description fully compensates: it explains what 'query' matches against with a concrete example ('speaker' -> alias 'the speaker'), why 'limit' exists (broad substrings like 'the' match thousands), and that 'offset' is safe because ordering is stable alphabetical.

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 and resource (substring search over subject names and aliases) plus the matching mode (case-insensitive). An agent can distinguish it from subjects_list or subjects_show by the search semantics, but the description never names those siblings explicitly.

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?

Usage is only implied: 'substring search' suggests search-when-you-don't-know-the-name, versus subjects_list for enumeration or subjects_show for a known subject. There is no explicit when-to-use, when-not-to-use, or alternative routing statement despite 16 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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