GigNGo Local Services Marketplace

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

With no annotations, the description carries the behavioral burden and discloses a lot: returned per-task fields, the privacy-offset of coordinates, the 'details' note being usually most specific, applicant counts, and time-to-first-applicant. It does not state side effects or the meaning of the 'priority' sort option, so it is not fully exhaustive.

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 front-loaded with the core purpose and every clause adds information, but the second sentence is a very long enumeration that packs many field names into one list. It is dense rather than concise, though nothing is 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 tool with 7 optional parameters and no output schema, the description is unusually complete: it explains what fields appear in results, what filters exist, and the zero-argument default. The main gap is the omitted 'priority' sort enum value, whose semantics are left undefined in both the description and 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 description coverage is 100%, so the parameter schema already documents each field; the description adds a broad orientation ('Filter by category, state, city, age or applicant count') and the no-arguments default. This matches the baseline 3 because the description does not materially deepen per-parameter semantics 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 opening verb-resource pair is precise: 'Browse open consumer tasks (jobs) posted on GigNGo that workers can apply to.' It also names the task content and filter dimensions, making it easy to distinguish from sibling tools like list_service_categories or search_local_workers.

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 says exactly when to call bare ('call with no arguments for the most recent open tasks nationwide') and enumerates filter dimensions and common sort orders. It does not explicitly name alternatives or exclusion conditions, but the use case is unambiguous for a browsing 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, the description carries the full burden of behavioral disclosure. It accurately describes a read-only availability check, specifies return contents (worker counts per service), and gives required input formats. It does not disclose potential edge cases like zero counts, but this is adequate for a simple check 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 description is three sentences with no wasted words. It front-loads the core purpose, then adds usage context, then parameter guidance. Each sentence earns its place.

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 purpose, output (worker counts), required parameter format, optional parameter behavior, and target geography. Without an output schema, this is sufficient for an agent to call the tool correctly. Minor omissions like behavior when no workers exist are not critical for this simple check.

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 input schema already has 100% coverage and documents the state and city parameters with examples and instructions to omit city for state-wide availability. The description largely repeats this information, adding little beyond what the schema provides, so a baseline of 3 is appropriate.

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 verb ('Check'), resource ('the 30 service categories'), scope ('US state or city'), and output ('worker counts per service'). It clearly differentiates from siblings like search_local_workers by stating it is 'useful before searching' and answers availability questions.

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 states when to use it: before searching or to answer 'can I get X done in Y?'. It provides clear context but does not name alternative tools or explicitly state when not to use it, so it falls short of a 5.

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 carries the full behavioral burden and does so well: it explains that the heat score combines applicant density with response reliability and is shrunk toward the platform average so a single lucky job cannot outrank a real market. It also tells the agent to read heat alongside confidence and previews the returned metrics, which goes well beyond the tool name.

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 tight and front-loaded: it opens with the core output metrics, then explains the ranking caveat, then gives the business use case and the sibling distinction. Every sentence contributes useful information and there is no filler.

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 read-only analytical tool with no annotations and no output schema, this is close to sufficient: it names the returned metrics, the heat-score behavior, and the main use case, while the schema covers filters and limits. The exact time window and precise machine-readable output field names are left implicit, so an agent may still need to infer some response structure.

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 input schema already describes all four parameters at 100% coverage, so the baseline is 3. The description adds meaning beyond the schema by explaining what heat and confidence represent and implying that heat is the default ranking score, while leaving limit, state, and minJobs to the schema's own 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?

The description opens with 'Find which US cities are actually converting' and then lists the concrete metrics: jobs posted, locals applied, reply share, and median hours to first applicant. This gives a specific verb, resource, and measurable outcome, and the closing complement comparison distinguishes it from the worker-counting service-availability tool.

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 says to use this tool to decide where supply is dense versus where jobs go unanswered, which is a clear trigger. It also names get_service_availability as the alternative and contrasts measuring response with counting workers, though the sibling list shows check_service_availability, so the routing is slightly inconsistent.

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 clearly states what the tool returns—platform overview, category count, coverage, app links, and API documentation URLs—which is the key behavioral trait for an info-only tool. It does not mention side effects or rate limits, but none are relevant for this read-only overview.

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 sentences with no filler. The first sentence front-loads the tool's purpose and deliverables, and the second sentence provides a clear usage trigger. Every word earns its place.

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 zero-parameter informational tool, the description is complete: it lists the major content areas and states when to invoke it. There is no output schema, but the enumerated items give an agent a solid expectation of the response. A response format note would be a minor enhancement, not a necessity.

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 tool has zero parameters, so the empty schema is fully descriptive and the baseline is 4. The description adds no parameter details because none are needed, and it instead clarifies the scope of the returned information, which is more valuable here.

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 uses a specific verb ('get') and resource ('overview of the GigNGo platform'), then enumerates concrete contents: service category count, US coverage, app links, and API documentation URLs. It also explicitly frames the tool for general 'what is GigNGo' questions, which distinguishes it from the task- and worker-oriented siblings.

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 usage context: 'Call this for general "what is GigNGo" questions.' This tells an agent when to use it, though it does not explicitly name alternatives or state when not to use it. The guidance is sufficient given the sibling tools are clearly more specialized.

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 carries the behavioral burden. It discloses that the profile is public and the operation is a read-only 'get', and it lists the return fields. It could additionally mention invalid-slug or not-found behavior, but for a simple public-profile lookup the transparency is adequate.

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?

Three focused sentences: purpose first, return contents second, parameter provenance last. There is no filler or redundant restatement of the tool name, and each sentence adds genuinely useful information.

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, read-only lookup with no output schema, the description covers the essential ground: what the tool does, what it returns, and how to obtain a valid slug. No critical information needed for correct invocation 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?

The schema already documents the slug parameter fully, so the baseline is 3. The description adds valuable guidance by specifying where slugs originate and giving the URL pattern, which helps the agent construct a valid call beyond the schema alone.

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 and resource: 'Get the full public profile of a single GigNGo worker by their profile slug.' It is clearly distinct from siblings like search_local_workers, which is a search/browse operation, and it enumerates the profile fields to remove ambiguity.

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 tells the agent where slugs come from—search_local_workers results or gigngo.org profile URLs—which implies this tool is the detail-lookup step after a search. It does not explicitly state when not to use alternatives, but the read-by-slug vs. browse/search distinction is clear from context.

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 carries the burden of behavioral disclosure. It states the tool returns all 30 categories with their slugs, which tells the agent exactly what to expect. It does not describe ordering or potential changes, but for a zero-parameter list tool this is sufficient.

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 concise sentences with no filler. Key facts are front-loaded: the exact number of categories, the resource type, and example slugs. Every sentence earns its place.

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 zero-parameter, no-output-schema tool, the description fully covers what the tool returns and why an agent would invoke it. Nothing essential 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?

There are no parameters, so the baseline is 4. The description adds value by explaining how the returned values feed into sibling tools' parameters, even though this tool itself takes no input.

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?

Description states a specific verb ('List'), a concrete resource ('all 30 GigNGo service categories'), and gives real examples of the returned slugs. It clearly differentiates this tool from siblings like search_local_workers and browse_open_tasks by identifying it as the category-discovery tool.

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 says when to call it: to discover valid values for the `service` parameter of search_local_workers and the `category` parameter of browse_open_tasks. It does not explicitly discuss when not to use it or name alternatives, but the guidance is clear and actionable.

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 of behavioral transparency. It discloses the return type (public worker profiles with specific fields), sorting order (by rating), and pagination behavior (returns up to `limit` workers). It implies a read-only operation through 'Search' and 'Returns', though it does not explicitly state safety or mention potential errors or empty results.

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 and well-structured. It front-loads the core purpose, then details the output and provides usage notes. Every sentence contributes meaningful information—the return fields, sorting, pagination, and the relation to list_service_categories. There is no redundant filler or overly technical jargon.

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 is contextually complete for the given complexity. It covers the input parameters, output content, sorting, pagination, and a related tool. It does not mention error handling or authentication, but these are not critical for a simple search operation. The lack of an output schema is acceptable because the description enumerates the expected fields.

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 covers 100% of parameters and already includes descriptions. The tool description adds value by providing concrete examples for state and city slug formats, reiterating the default limit, and explaining the service slug context. While the description largely echoes the schema, it enhances comprehension with practical examples and usage context, justifying a score above the baseline.

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: searching for local workers by service category and location on GigNGo. It specifies the resource (workers), the action (search), and the filtering criteria. The description also distinguishes it from the sibling tools by mentioning the returned profile fields, sorting by rating, and pagination, making its role unique.

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 a clear pointer to use list_service_categories when the service slug is uncertain, which is helpful prerequisite guidance. However, it does not explicitly state when to choose this tool over other siblings like get_worker_profile or browse_open_tasks. The context implies these alternatives, but explicit exclusions are missing, so it falls short of a perfect score.

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