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Dorit

get_more_tools

Read-onlyIdempotent

Check for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.

Input Schema

NameRequiredDescriptionDefault
contextYesA description of your goal and what kind of tool would help accomplish it.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds no behavioral context about what invoking it does (does it return candidate tools, register them, or just log the request?) beyond the fallback hint, which is usage rather than behavior.

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?

A single front-loaded sentence with the action first and the qualifier second; every clause earns its place and there is no padding.

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?

No output schema exists, so the description should explain what a caller gets back from a tool-discovery call. It does not, leaving the main remaining gap for an openWorld meta-tool; the parameter docs partly compensate via the well-specified 'context' field.

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 both parameters ('context' and 'llm_model', including the useful 'never guess' instruction) are fully documented in the schema. The description adds no parameter meaning beyond that, 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+resource: 'check for additional tools' for tasks needing specialized capabilities. It is distinguishable from all siblings (scheduling, weather, events), but it does not say what the check actually returns or whether tools are enabled as a result.

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?

Gives a clear trigger condition ('whenever your task might benefit from specialized capabilities') plus a non-obvious nuance ('even if existing tools could work as a fallback'), which preempts the common failure of never probing. It does not state when NOT to call it.

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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