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reasoningdelegationhigh

The high-effort reasoning agent is built for complex tasks that require deep, multi-step analysis and robust problem-solving. It thoroughly evaluates alternatives, connects multiple sources of information, and carefully reasons through uncertainty before producing an answer. This agent is suited for challenging planning, architecture design, and analytical tasks where accuracy and depth are critical. Expected Runtime: ~60s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses expected runtime (~60s) and methodology (evaluates alternatives, connects sources, reasons through uncertainty), which is useful. However, it does not mention whether the tool is read-only, whether it has side effects, or what the return format looks like. The behavior is partially transparent but lacks critical details.

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 description is appropriately concise: two sentences plus a runtime note. It front-loads the purpose and lists use cases, with the runtime as a separate line. No extra fluff, and the structure is clear.

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

Completeness2/5

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

For a tool with a single required parameter and no annotations, the description should explain what the query should contain and what the output will be (especially since an output schema exists but is not provided). It also lacks any prerequisites or limitations. The description is insufficient for an agent to correctly invoke the tool without additional information.

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

Parameters1/5

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

The description says nothing about the 'payload' or 'query' parameters. The schema itself has no parameter descriptions (0% coverage), so the agent receives no guidance on what to put in the query field. This is a critical gap, as the description fails to compensate for the missing schema information.

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 clearly states that this is a high-effort reasoning agent for complex tasks requiring deep analysis, and specifies typical use cases like planning, architecture design, and analytical tasks. It differentiates implicitly through the 'high-effort' label and runtime, but does not explicitly contrast with the low and medium variants, so it falls short of 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 provides clear context on when to use it ('suited for challenging planning, architecture design, and analytical tasks where accuracy and depth are critical'), but does not explicitly state when NOT to use it or mention alternatives like reasoningdelegationlow or reasoningdelegationmedium. The guidance is present but not exhaustive.

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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TDQS

C2.7/5.0
Disambiguation3/5

Several tools overlap in purpose, particularly the research/analysis agents (constructivecritic, firstprinciplesanalyst, scientificresearchagent, researchagent) and the three reasoningdelegation agents, which differ only by effort level. Some tools like 'exploitagent' and 'testagent' have vague descriptions that don't clarify distinct roles. However, many tools are clearly distinct (e.g., campbuddy vs. smart_fridge___nutrition), and the core router tools (discover_agents, a2a_call_agent, wait_for_task) are well-defined.

Naming Consistency2/5

Naming is inconsistent: some tools use snake_case (a2a_call_agent, discover_agents, wait_for_task) while most others are camelCase or concatenated lowercase (browsernavigationagent, campbuddy, reasoningdelegationhigh). There's also odd naming like 'smart_fridge___nutrition' with triple underscore, and simple names like 'testagent' and 'exploitagent'. No consistent convention exists across the set.

Tool Count4/5

With 24 tools, this is near the upper limit but still reasonable for an agent router that hosts many pre-defined specialized agents. The core router functions (discover, call, wait) are supplemented by a diverse set of agent tools. It's borderline heavy but each tool represents a distinct agent or action, so it's acceptable.

Completeness4/5

The router functionality is well-covered: discovery (discover_agents), synchronous calling (a2a_call_agent), asynchronous handling (wait_for_task), and skill lookup (search_skills/get_skill) for extension. Missing are explicit cancellation or task management tools, but core workflows are supported. The presence of domain-specific agents (campbuddy, silpo_home_restaurant) doesn't detract from router completeness.

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