Agent Budget Router
Server Details
Budget-aware AI agent research router with provider selection, validation, and automatic fallback.
- Status
- Healthy
- Uptime
- 100.0% over 20 days
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
Execute_task and optimize_task both concern research tasks and provider selection, but one selects a free provider and the other executes using a validated paid provider. The boundary is mostly clear, though an agent could briefly confuse them if the descriptions are not read carefully.
Both tools follow a consistent verb_noun snake_case pattern (execute_task, optimize_task). There are no deviations, making the naming predictable and easy to parse.
Two tools is borderline thin for a server named 'Agent Budget Router'—one selects a provider and one executes. While each tool has a distinct role, the surface lacks supporting operations and feels minimal.
The core optimize-then-execute workflow is covered, but there is no way to check remaining budget, list available providers, or retrieve task status or history. These gaps could lead to overspending or dead ends for an agent.
Available Tools
2 toolsexecute_taskCInspect
Execute a research task using a validated provider. This tool costs $0.01 USDC per successful call.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| budget | No | ||
| provider | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral burden. It helpfully discloses a per-call cost ($0.01 USDC per successful call), but omits auth/permission needs, what happens to the task, how budget is enforced, and what a caller gets back.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with zero filler, and the core action is front-loaded. Efficient, though the second sentence is the only substantive detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no annotations, no output schema, and 0% schema coverage, the description is too thin. It should explain task input expectations, budget behavior, and provider selection to be callable correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% across 3 parameters, so the description must compensate and does not. 'Validated provider' vaguely points at the provider enum, but the task format and the budget parameter's role and default (0) are left unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (execute) and resource (research task), plus a qualifier about the provider. Clear on its own, but it offers no differentiation from the sibling optimize_task, so an agent cannot tell when this one is the right choice.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no when-to-use guidance and no mention of the alternative optimize_task. The only hint at context is 'using a validated provider,' which does not tell the agent which conditions select this tool over its sibling.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| budget | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| task | No | |
| error | No | |
| budget | No | |
| status | Yes | |
| results | Yes | |
| attempts | Yes | |
| provider | No | |
| fallback_used | Yes | |
| requested_provider | No | |
| zero_out_of_pocket | Yes | |
| recommended_provider | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It says it selects a 'free provider' but does not disclose whether the call itself spends budget, reserves a provider, or has any side effects, nor any rate limits or auth requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single clean sentence with the selection criteria front-loaded and no filler. It is appropriately sized, though the phrase 'best available free provider' stays abstract where one concrete detail would have helped.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema covers return values, and the tool is low-complexity with two params, but with no annotations the description should at least clarify the relationship to execute_task and the meaning of budget. As written, an agent cannot confidently place this step in the workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the schema documents neither of the two parameters. The description partially compensates by naming 'task relevance' and 'budget' as the selection inputs, implicitly mapping to the task and budget fields, but gives no format, unit, or default semantics for budget.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action (choose the best available free provider) and the resource (AI-agent research task), and frames the selection criteria (relevance and budget). It is inferable but not explicit that this is the pre-execution routing step distinct from the sibling execute_task, so sibling differentiation is weak.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to call this versus execute_task, nor any stated prerequisites or ordering (e.g., call before execute_task). The relationship to its only sibling is left 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.
2 tool updates
- Changed
execute_task2 fields changed- changed
Input schema / $schemaPrevious value: -"https://json-schema.org/draft/2020-12/schema"New value: +"http://json-schema.org/draft-07/schema#" - changed
Output schema / (root)Previous value: -{ - "$schema": "https://json-schema.org/draft/2020-12/schema", - "additionalProperties": false, - "properties": { - "attempts": { - "items": { - "type": "string" - }, - "type": "array" - }, - "budget": { - "type": "number" - }, - "error": { - "type": "string" - }, - "fallback_used": { - "type": "boolean" - }, - "provider": { - "type": "string" - }, - "recommended_provider": { - "type": "string" - }, - "requested_provider": { - "type": "string" - }, - "results": { - "items": { - "additionalProperties": false, - "properties": { - "snippet": { - "type": "string" - }, - "title": { - "type": "string" - }, - "url": { - "type": "string" - } - }, - "required": [ - "title", - "url" - ], - "type": "object" - }, - "type": "array" - }, - "status": { - "type": "string" - }, - "task": { - "type": "string" - }, - "zero_out_of_pocket": { - "type": "boolean" - } - }, - "required": [ - "status", - "fallback_used", - "results", - "attempts", - "zero_out_of_pocket" - ], - "type": "object" -}New value: +null
- Changed
optimize_task2 fields changed- changed
Input schema / $schemaPrevious value: -"https://json-schema.org/draft/2020-12/schema"New value: +"http://json-schema.org/draft-07/schema#" - changed
Output schema / $schemaPrevious value: -"https://json-schema.org/draft/2020-12/schema"New value: +"http://json-schema.org/draft-07/schema#"
2 tool updates
- First observed
execute_task - First observed
optimize_task
Related MCP Connectors
Outcome-first agent fallback: free discovery, minimal routing, declared costs, verified execution.
Web search for AI agents — one tool across 6 engines, routed to the cheapest + cached.
Multi-LLM AI Research & Analysis — smart routing, consensus analysis, due diligence reports
Fan out deep research across multiple AI providers, synthesize into one unified report.
Related MCP Servers
- FlicenseNot gradedqualityAmaintenanceIntelligently routes AI tool calls (search, extract, browse) to the cheapest capable provider by default with automatic failover and audit receipts, enabling cost-effective agent workflows.2-
- AlicenseNot gradedqualityBmaintenanceEnables agents to run bounded research experiments across configurable model providers and search modes, with cost estimation, budget gating, durable paginated results, idempotent runs, and cancellation. Exposes seven tools for discovering targets, estimating and starting runs, polling status, and retrieving citations, sources, and usage from stored evidence.Apache 2.0
- AlicenseBqualityAmaintenanceOne endpoint, five search providers. Search broker for AI agents with automatic fallback, RRF ranking, and budget enforcement. The LiteLLM of web search.1365 PyPI5MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to perform grounded web research with injection resistance, claim verification, and cost-aware routing through MCP tools like web_search, fetch_url, extract_claims, and check_grounding.MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.