TokenLab MCP Server

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds that data comes from the 'live public catalog,' implying no auth and fresh data, which is useful context, but says nothing about return format or rate limits.

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 front-loaded sentence listing the compared attributes with zero filler. Appropriately sized for a simple read-only comparison tool, though nothing about it is notably well-structured beyond being terse.

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 one-parameter read-only tool with full schema coverage and annotations, the description tells the agent what is compared and where the data comes from. No output schema exists, so return values need not be described, though a hint at result shape would have helped.

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 100% for the single 'models' parameter, and the schema itself documents the 1-8 range with an example. The description only echoes 'specific TokenLab model IDs,' adding no syntax or format detail beyond the schema. Baseline 3 applies.

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 (compare) and resource (TokenLab model IDs) plus the dimensions compared: price, context window, capabilities. Clear and distinguishable from siblings like generate_tokenlab_endpoint_example or open_tokenlab_model_explorer, though it never names them explicitly.

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?

Implicitly scoped to comparing 1-8 specific model IDs, but offers no when-to-use guidance, no mention of alternatives, and no exclusions. An agent must infer the comparison use case from the verb alone.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the agent knows this is a safe, local, non-networking operation. The description adds the fact that the output is a copyable example, but says nothing about what happens when the model is unsupported or how validation failures surface. Some added value, no contradiction.

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 efficient sentence with the action and artifact front-loaded; nothing is repeated or wasted.

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 two-parameter, read-only generation tool with no output schema, the description plus the rich schema fields cover what an agent needs to invoke it. The only minor gap is that the return shape (a text snippet) is implied by "copyable" rather than stated.

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 both parameters, including the endpoint enum values and the model catalog requirement, are already fully documented in the schema. The description's mention of "OpenAI-compatible or native TokenLab endpoints" merely echoes the endpoint enum, adding no format or syntax detail. Baseline 3 applies.

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 artifact: "Generate a copyable cURL example" for named endpoint families. An agent immediately knows this produces example code rather than calling an API or comparing models, so it is distinguishable from the siblings (compare_tokenlab_models, open_tokenlab_model_explorer) even though they are not named.

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 statement of when to reach for this tool versus the sibling tools, nor any prerequisite (e.g. model must exist in the live catalog) or exclusion. Usage is only implied by the artifact type.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely new context — the data is drawn from the "live public catalog," implying fresh results and no authentication — but says nothing about result volume, pagination, or ordering.

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 sentence that front-loads the action and resource, then layers in the return fields and data source. No filler, no repetition of the title.

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?

No output schema exists, but the description compensates by enumerating the salient returned fields (context window, capabilities, platform price). Combined with a 0-required-parameter browse tool and annotations covering safety, this is nearly complete; only result-set behavior (limits, ordering) is unaddressed.

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 both parameters (query, category) are already fully documented with examples and category values in the schema. The description only echoes them at a high level and adds no syntax, matching behavior, or fallback semantics beyond what the schema provides. Baseline 3 applies.

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?

Specific verb ("Browse") + resource ("TokenLab models") plus the fields returned (context window, capabilities, platform price) and the data source (live public catalog). It does not explicitly name how it differs from siblings like compare_tokenlab_models, but the browse/search framing makes the distinction reasonably inferable.

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 phrase "by category or search query" implies the two entry modes (browse vs. search), which is mild usage guidance. However, it never states when to reach for this tool versus compare_tokenlab_models or generate_tokenlab_endpoint_example, and offers no exclusions or prerequisites.

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