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Agent Budget Router

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Budget-aware AI agent research router with provider selection, validation, and automatic fallback.

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Status
Healthy
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL

TDQS

B3.2/5.0

Scored across 2 tools

Disambiguation4/5

optimize_task selects a provider, while execute_task runs the task with fallback, giving mostly distinct purposes. However, execute_task's fallback implies internal provider selection, so an agent might confuse whether it also optimizes.

Naming Consistency5/5

Both tool names follow a consistent verb_noun snake_case pattern: execute_task and optimize_task. No deviations or mixed conventions.

Tool Count3/5

Two tools is borderline thin for a budget-routing service. While each has a role, auxiliary tools like listing providers or checking budget status would make the set feel more complete.

Completeness4/5

The core lifecycle—choose a provider and execute with validation/fallback—is covered. Minor gaps exist, such as no way to inspect available providers or current budget state.

Available Tools

2 tools
execute_taskCInspect

Execute a research task using a free provider, validate the result, and automatically fall back to another free provider if needed.

ParametersJSON Schema
NameRequiredDescriptionDefault
taskYes
budgetNo
providerNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
taskNo
errorNo
budgetNo
statusYes
resultsYes
attemptsYes
providerNo
fallback_usedYes
requested_providerNo
zero_out_of_pocketYes
recommended_providerNo

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, and it does disclose valuable behavior: result validation and automatic fallback to a second free provider. However, it omits cost/permission implications, what triggers a fallback, and whether the provider choice is honored or overridden.

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?

A single efficient sentence with the action front-loaded and no filler. It could be slightly longer if that length bought parameter or when-to-use clarity, but as written it is tight.

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?

An output schema exists so return values need not be described, but with 0% schema coverage and no annotations, the description leaves all three parameters undefined and gives no guidance on provider selection or budget behavior. It is under-specified for a task-execution tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate and does not. The 'provider' enum (wikipedia_free vs openalex_free) and the 'budget' parameter's meaning and units are entirely unexplained anywhere.

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 pairs a specific verb ('Execute') with a clear resource ('research task') and adds the processing pipeline (validate, fall back). It distinguishes itself reasonably from the sibling optimize_task, though it never names it explicitly.

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 statement of when to use this tool versus optimize_task, nor any prerequisites or exclusions. Usage is only implied by the description of the internal fallback behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

optimize_taskCInspect

Choose the best available free provider for an AI-agent research task based on task relevance and budget.

ParametersJSON Schema
NameRequiredDescriptionDefault
taskYes
budgetNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
taskNo
errorNo
budgetNo
statusYes
resultsYes
attemptsYes
providerNo
fallback_usedYes
requested_providerNo
zero_out_of_pocketYes
recommended_providerNo

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are absent, so the description carries the full behavioral burden. It mentions selecting a 'free provider' but does not disclose side effects, whether it performs any mutation, API/auth requirements, or fallback behavior if no free provider is available.

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 no wasted words. The verb 'Choose' comes first, and the criteria are stated compactly.

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?

Output schema exists, so return values need not be explained. However, for a tool with a sibling execute_task and no annotations, the description omits when to route here instead, and leaves budget semantics and behavioral safety unaddressed.

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?

With 0% schema description coverage, the description partially compensates by naming both parameters conceptually: 'task' for relevance and 'budget' for cost. However, it does not explain budget units, the default value of 0, or task format, leaving meaningful gaps.

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: choose the best available free provider for an AI-agent research task. It adds selection criteria (task relevance, budget), making the purpose clear, but it does not differentiate from the sibling execute_task.

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?

No explicit guidance on when to use this tool versus execute_task or other alternatives. The description implies it is for provider selection before research, but leaves the routing decision entirely to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedexecute_task
    • First observedoptimize_task

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