Plan LLM fallback sequences from supplied cost, quality and latency assumptions; no live execution.
Score is being calculated.
Retrieve a public tool reference by the ID returned from search. Contains methodology, limits, official source links and fictional worked examples; no user records. Cite the returned canonical URL.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| url | Yes | |
| text | Yes | |
| title | Yes | |
| metadata | Yes |
Route Lab compares short supplier sequences using entered cost, success and latency assumptions. It checks the worst-case total against your budget before sorting feasible options by expected cost. It is an offline planning tool; no model or payment is called. Offline planning only. No live requests, model judge, payment, dynamic bandit or production routing. Retrieve route with fetch or read its example resource to obtain exact inputs. Parameters are processed remotely without application persistence.
| Name | Required | Description | Default |
|---|---|---|---|
| budget | Yes | Total budget (preserve source text; decimal amounts must stay strings) | |
| currency | Yes | Currency (preserve source text; decimal amounts must stay strings) | |
| candidates | Yes | Candidates | |
| minSuccess | Yes | Minimum success probability (0–1) | |
| maxAttempts | Yes | Maximum attempts | |
| maxLatencyMs | Yes | Total latency bound (ms) |
| Name | Required | Description |
|---|---|---|
| report | Yes | |
| toolId | Yes | |
| version | Yes | |
| citation | Yes | |
| revision | Yes | |
| processing | Yes | |
| limitations | Yes | |
| evidenceStatus | Yes | |
| officialReferences | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds important context beyond those: it is offline, calls no model or payment, performs no live requests, and processes parameters remotely without application persistence. This is exactly the kind of behavioral disclosure an agent needs to avoid misusing the tool.
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?
The main function is front-loaded and clear, but there is redundancy: 'It is an offline planning tool; no model or payment is called' is immediately followed by 'Offline planning only. No live requests, model judge, payment, dynamic bandit or production routing.' This repeats the offline and payment exclusions without adding new information. Otherwise, the description is compact.
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?
With an output schema present, full parameter schema, and strong annotations, the description does not need to explain return values. It covers offline behavior, non-persistence, and how to obtain exact inputs via fetch or an example resource, giving an agent sufficient context to call the tool 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 100%, so the baseline is 3. The description adds relational meaning by framing cost, success, and latency as assumptions, explaining that budget is checked against the worst-case total, and stating that feasible options are sorted by expected cost. This helps the agent understand how the parameters interact beyond their individual schema descriptions.
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 and resource: 'compares short supplier sequences' using cost, success, and latency assumptions, then checks worst-case total against budget and sorts feasible options by expected cost. It also differentiates itself from siblings by explicitly declaring it is an offline planning tool and excluding live requests, model judge, payment, dynamic bandit, and production routing.
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?
The description explicitly scopes use to offline planning and enumerates exclusions: no live requests, model judge, payment, dynamic bandit, or production routing. It also names an alternative path for obtaining inputs: 'Retrieve route with fetch or read its example resource,' giving an agent clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search this server’s public AI/Web3 tools and citation pages. English and Chinese names supported. Empty query lists the available worksheets.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description |
|---|---|---|
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds valuable behavioral context: bilingual (English and Chinese) name support, public scope, and the empty-query listing behavior. These details go beyond what annotations or schema provide without contradicting them.
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?
The description is two clean sentences with no filler. The primary action is front-loaded, and the supporting usage details are kept minimal and relevant.
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 single-parameter search tool with annotations and an output schema, the description is complete: it defines the search scope, supported query forms, and the empty-query behavior. No essential information for invoking the tool correctly is missing.
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 description carries the burden of explaining the query parameter. It clarifies that queries are names, that English and Chinese are accepted, and that an empty query has special meaning. It does not fully specify matching behavior (e.g., exact vs partial), but it compensates well for the schema gap.
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 states a specific verb ('Search') and resource ('this server’s public AI/Web3 tools and citation pages'), making the tool’s function immediately clear. It is distinct from all sibling tools, which focus on checking permissions, comparing costs, or reading profiles rather than searching a corpus. The title and description align well.
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?
The description clearly indicates when to use the tool: when searching for AI/Web3 tools or citation pages on this server. The note about empty query listing available worksheets provides a concrete usage scenario. It does not explicitly name alternatives or exclusion conditions, so it misses the top score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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