Mockbird

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

With no annotations, the description details key behaviors: seeding fake data, two configuration methods, and seed constraints. Does not mention error conditions or return values, but adequately covers the tool's operation.

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?

Four-sentence paragraph, front-loaded with purpose. Efficient but could be structured with bullet points for clarity. No unnecessary words.

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?

Covers purpose, parameters, and usage, but lacks details on return values, error handling (e.g., resource already exists), or side effects. Adequate for a moderate-complexity tool with no output schema.

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%, baseline 3. Description enhances understanding by listing built-in templates, explaining fields structure with types, and clarifying seed defaults/max. Adds value beyond schema.

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 explicitly states 'Add a resource (collection) to a project and seed it with realistic fake data', using specific verb and resource. It differentiates from siblings like 'import_data' and 'write_record' by focusing on seeding fake data.

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?

Describes when to use (add resource with fake data) and how to use via template or fields. Provides seed limits and defaults. Lacks explicit exclusions or alternative tool references.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It thoroughly discloses side effects: adminKey shown only once (must be saved), mock API is live at baseUrl with standard REST CRUD, CORS enabled, no auth. However, it doesn't explicitly state this is a creation/mutation operation or discuss reversibility/deletion behavior.

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 well-organized and front-loaded with the core purpose (create project + return payload). Every sentence earns its place: adminKey warning, preset explanations, live API details. No wasted words despite the richness of content.

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?

Comprehensive for a 2-param creation tool with no output schema. It covers return format, authentication implications (adminKey), presets with concrete details, live API behavior (CRUD, CORS, no auth), and how this tool fits with siblings (add_resource, custom_route, snapshots). No significant gaps remain for an agent to invoke 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 coverage is 100%, so parameters are well-documented in the schema. The description adds substantial value beyond the schema: explaining exactly what each preset seeds (blog, ecommerce, saas, openai specifics including SSE streaming and 1536-dim vectors), what 'omit preset' means (starter project with items resource), and what 'blank' produces. This exceeds the baseline 3 for high coverage.

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 clearly states 'Create a new mock REST API project' and specifies the return payload {id, adminKey, baseUrl, resources[]}. It distinguishes itself effectively from siblings (add_resource, import_data, etc.) by explaining that this is the project initialization tool while others manipulate an existing project.

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?

Excellent guidance. It explicitly explains when to use each preset (blog, ecommerce, saas, openai) and when to omit preset for a starter project vs 'blank' for a truly empty one. It clarifies the admin operations that require adminKey (add_resource, custom_route, snapshots), giving the agent concrete decision criteria.

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?

With no annotations, the description fully discloses key behaviors: body template syntax with variable substitutions, triple braces for raw JSON, method/status/contentType defaults, precedence over resource routes, and the 20-route limit.

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 dense but informative, delivering all key points in one paragraph; could benefit from slight restructuring (e.g., bullets) for easier scanning.

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?

Given 8 parameters and no output schema, the description covers template syntax and limits but omits what the tool returns (e.g., the created route) and error handling, leaving the agent somewhat uncertain about the response.

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 75%, and the description adds significant meaning (e.g., template syntax for 'body', default values for method/status/contentType), though it does not detail 'project' or 'adminKey' beyond the schema.

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 clearly states it 'Define a custom endpoint on a project' with concrete examples like '/health, /config/:key, or a catch-all /webhooks/* request bin', and distinguishes from resource routes by noting precedence and fallback behavior.

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?

It explains when to use (for custom endpoints) and notes constraints ('Max 20 routes/project') and precedence rules, but does not explicitly compare to sibling tools or state when not to use.

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?

No annotations exist, so the description carries full burden. It discloses auto-detection behavior, max file size (512 KB), and return object fields (id, adminKey, baseUrl, warnings). It also explains how Postman examples and CSV types are handled. This is solid behavioral context.

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 a single paragraph, efficient and front-loaded with the main purpose. It covers all key points without wasted words. Could be slightly more structured but is effective.

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?

Given the tool's complexity (multiple artifact types, auto-detection, parameters), the description is quite complete. No output schema exists, but return values are described. Sibling tools hint at missing guidance but overall the description provides sufficient context for an agent.

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 baseline is 3. The description adds meaning: explains 'content' accepts various artifact types, 'seed' default/max for OpenAPI, and 'resource' is CSV-only with default 'items'. This enhances parameter understanding.

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 clearly states the tool's purpose: 'Create a live mock API from existing artifacts.' It lists specific artifact types (OpenAPI, Swagger, json-server, Postman, CSV/TSV) and what each produces, distinguishing it from sibling tools like 'create_project' or 'add_resource'.

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 implies usage (when you have an existing artifact) but lacks explicit guidance on when not to use this tool or alternatives. No exclusions or comparisons to siblings are provided.

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?

With no annotations, the description must disclose behavior. It states it is public (no adminKey needed) and lists return contents. It does not mention side effects, but as a read operation, this is acceptable.

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 sentences with no extraneous information. The first sentence lists all return elements, the second adds auth info and an example. Efficient and well-structured.

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 simple tool with one parameter and no output schema, the description is complete: it explains what is returned, that no authentication is required, and provides an example. No gaps remain.

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 parameter. The description adds context by calling it a 'project id' and providing an example ('demo'), which adds value beyond the schema's bare description.

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 clearly states it gets a project's public root index, listing resources, routes, auth mode, and export links. It uses a specific verb ('Get') and resource ('project's public root index'), distinguishing it from sibling tools like query_records.

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 notes that no adminKey is needed, indicating it is a public read operation. It also suggests trying the 'demo' project. While it does not explicitly contrast with alternatives, the context is sufficient for usage.

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?

With no annotations provided, the description carries the full burden and excels. It discloses failure simulation (mock_status, mock_delay, mock_chaos, mock_seq, mock_jitter), envelope wrapping (mock_envelope), and default limit behavior—all beyond the schema.

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 dense yet well-organized into a single paragraph. It front-loads the purpose and then systematically covers parameter details, failure simulation, and defaults—every sentence earns its place without redundancy.

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 description covers all query capabilities, failure modes, pagination, and projections. With no output schema, it doesn't detail the exact return shape, but the mock context and envelope mention provide adequate context for an agent to understand what to expect.

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?

The schema only describes params as 'Query params as key→value,' but the description details exact filter suffixes, full-text search, sorting, pagination, field projection, relation expansion, and all mock controls. This adds immense meaning beyond the schema.

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 opens with 'GET records from a mock resource,' clearly stating the verb, resource, and mock nature. This distinguishes it from sibling write tools like write_record and create_project, making the purpose unambiguous.

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 gives clear context for querying (e.g., 'Pass id to fetch a single record') and mentions pagination defaults. However, it does not explicitly name alternatives or exclusion criteria, relying on the GET verb and sibling names to imply read-only usage.

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?

With no annotations, the description clearly discloses the tool's behavior: saving entire dataset, exact restoration, list/delete operations, and read-only serving from snapshots. It does not cover potential side effects like overwriting existing snapshots or permission 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?

The description is concise, with two sentences covering core functionality and an advanced use case. No redundant information, and key details are front-loaded.

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 description covers primary use cases and the advanced mock_snapshot feature, but lacks details on return values (e.g., what list returns) and error scenarios. Given no output schema, this is a notable gap.

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?

The description adds meaning to the action enum (save, list, restore, delete) and notes the default naming for save. However, project and adminKey parameters remain unexplained, and schema coverage is only 25%, leaving gaps.

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 explicitly states the tool's purpose: creating deterministic test fixtures by saving, restoring, listing, and deleting snapshots of the entire project dataset. It clearly distinguishes from siblings by focusing on testing scenarios.

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 provides practical guidance, including an advanced use case for serving GET requests from a snapshot via query param, enabling parallel test scenarios. However, it does not explicitly name alternative tools or state when not to use this tool.

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?

With no annotations provided, the description carries the full burden. It discloses persistence behavior (writes persist) and explains the id requirement for PUT/PATCH/DELETE, which is not enforced in the schema (id is optional). This adds valuable behavioral context beyond the input schema.

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 extremely concise: two sentences that cover core functionality, persistence characteristics, and parameter requirements. Every sentence is necessary and informative with no redundancy.

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 description covers operations and persistence but omits details like return behavior (does it return the created/updated record?), error scenarios, or response format. Given the tool has no output schema and 5 parameters, a bit more completeness (e.g., 'returns the record' or 'empty body on success') would be helpful.

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 only 40% (id and body have descriptions). The description adds meaning by explaining which methods require which parameters (e.g., id for PUT/PATCH/DELETE, body for POST/PUT/PATCH). However, it does not clarify the purpose of the required 'project' and 'resource' parameters, leaving a 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 explicitly states the tool creates, updates, or deletes records, with clear mapping of HTTP verbs to operations (POST creates, PUT replaces, PATCH merges, DELETE removes). This distinguishes it from sibling tools like query_records, which are read-only, and other resource-management tools.

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 specifies when to use each method (e.g., POST for create, id required for PUT/PATCH/DELETE) and notes that writes persist unlike JSONPlaceholder/FakeStoreAPI. However, it does not explicitly contrast with sibling tools or advise when not to use this tool (e.g., for reading).

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