Voxpact MCP Server

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it requires authentication (Bearer token), explains the financial flow (funding via Stripe escrow), and describes the buyer-worker interaction model. However, it doesn't mention rate limits, error conditions, or what happens on creation failure.

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 efficiently structured in three sentences: authentication requirement, core functionality with funding mechanism, and job type differentiation. Every sentence adds value with zero wasted words, making it easy to parse.

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 complex mutation tool with 8 parameters, no annotations, and no output schema, the description does a good job covering the essential context: authentication, financial model, and job types. However, it doesn't explain what happens after creation (e.g., returns a job ID, confirmation message) or potential error scenarios, leaving some gaps.

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?

With 100% schema description coverage, the schema already documents all 8 parameters thoroughly. The description adds some context about job types ('direct = assigned to specific agent, open = agents bid') which aligns with the 'job_type' enum, but doesn't provide significant additional meaning beyond what's in the schema. Baseline 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 clearly states the specific action ('Create a new job'), identifies the resource ('on VoxPact'), and distinguishes it from siblings by explaining the job creation process including funding via Stripe escrow and worker delivery. It goes beyond a simple tautology of the name.

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 clear context about when to use this tool (for creating jobs with buyer/worker dynamics) and mentions the two job types (direct and open), but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools like 'submit_bid' or 'deliver_job'.

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 disclosure. It adds some context: deliverables are validated by LLM and payment is released from escrow, indicating a multi-step process with financial implications. However, it lacks critical details such as required permissions, error handling, whether the operation is idempotent, or what happens if validation fails. For a mutation tool with zero annotation coverage, this is insufficient.

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 front-loaded, stating the core purpose in the first sentence. The second sentence adds important behavioral context without redundancy. Both sentences earn their place by providing value, making it efficient and well-structured for quick understanding.

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 complexity of a job delivery tool with mutation behavior, no annotations, and no output schema, the description is incomplete. It covers the basic purpose and hints at validation and payment processes but omits details on response format, error conditions, side effects, and how it integrates with sibling tools. This leaves significant gaps for an agent to operate effectively.

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%, meaning all parameters are documented in the input schema. The description does not add any parameter-specific details beyond what the schema provides (e.g., it does not explain the structure of 'deliverable' or format of 'job_id'). According to the rules, with high schema coverage, the baseline score is 3, as the description does not compensate with extra semantic value.

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: 'Submit deliverables for a job.' It specifies the action (submit) and resource (deliverables for a job), making the intent unambiguous. However, it does not explicitly differentiate this from sibling tools like 'create_job' or 'submit_bid', which are related but distinct operations in the job workflow.

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 no guidance on when to use this tool versus alternatives. It mentions that deliverables are validated by LLM before payment, which implies a context of job completion, but does not specify prerequisites (e.g., job must be in a certain status), exclusions, or direct comparisons to siblings like 'send_message' or 'submit_bid'. This leaves the agent without clear usage rules.

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 states this retrieves 'public' data, implying it's a read-only operation accessible without special permissions, which is helpful. However, it lacks details on error handling (e.g., what happens if the agent_id is invalid), rate limits, or response format (e.g., JSON structure), leaving gaps for a tool with no annotation coverage.

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, efficient sentence that front-loads the core purpose ('Get a public agent profile') and lists key fields without unnecessary elaboration. Every word adds value, making it appropriately sized for a simple retrieval tool.

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 low complexity (one required parameter, no output schema, no annotations), the description is minimally adequate. It covers what data is returned but lacks details on behavioral aspects like errors or formatting. For a read-only tool with no output schema, it should ideally hint at the return structure, but it meets basic needs without being fully comprehensive.

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 has 100% description coverage, with the single parameter 'agent_id' documented as a UUID. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., it doesn't clarify format or sourcing of the UUID). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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 action ('Get') and the resource ('a public agent profile'), and lists specific fields included (trust score, capabilities, rating, completed jobs count, availability status). It distinguishes from siblings like 'search_agents' by focusing on retrieving a specific agent's profile rather than searching. However, it doesn't explicitly contrast with other read-only siblings like 'get_job_status' or 'get_open_jobs', preventing a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'get_agent_profile' over 'search_agents' (e.g., for detailed info on a known agent ID vs. finding agents by criteria), nor does it address prerequisites like needing the agent's UUID. Usage is implied by the tool name but not explicitly stated.

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 mentions that the tool returns 'status, participants, deliverables, messages, and payment state', which gives some insight into output behavior. However, it fails to disclose critical traits like whether this is a read-only operation (implied but not stated), potential error conditions, rate limits, or authentication requirements, leaving significant gaps for a tool that likely interacts with job data.

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 front-loaded, stating the core purpose in the first sentence and listing return values in the second. There is no wasted language, and it efficiently communicates key information. However, it could be slightly improved by integrating usage context or behavioral details without sacrificing brevity.

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 moderate complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output structure but lacks details on usage guidelines, error handling, or behavioral traits. Without annotations or an output schema, more context would be beneficial to fully guide an AI agent, but it meets the minimum threshold for a simple read operation.

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 has 100% description coverage, with the single parameter 'job_id' documented as a 'UUID of the job'. The description does not add any additional meaning beyond this, such as format examples or validation rules. Since the schema already provides adequate parameter information, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Get the current status and details of a job.' It specifies the verb ('Get') and resource ('job'), making it easy to understand what the tool does. However, it does not explicitly distinguish this tool from its sibling 'get_open_jobs', which might also retrieve job information, leaving some ambiguity about when to use one over the other.

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 no guidance on when to use this tool versus alternatives like 'get_open_jobs' or 'deliver_job'. It lacks context about prerequisites, such as needing a specific job ID, or exclusions, such as not being suitable for creating or modifying jobs. Without this information, users may struggle to select the correct tool among siblings.

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 full burden for behavioral disclosure. It mentions the return data structure (descriptions, budgets, deadlines, capabilities) but omits critical details like pagination behavior, rate limits, authentication requirements, or whether results are sorted. For a listing tool with zero annotation coverage, this leaves significant gaps.

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 efficiently structured in two sentences: one stating the purpose and scope, another detailing return values. It's front-loaded with the core function and avoids unnecessary verbiage, though it could be slightly more concise by integrating return details into the first sentence.

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 no annotations and no output schema, the description partially compensates by specifying return data fields. However, for a listing tool with 4 parameters, it lacks details on response format, error handling, or operational constraints. It's minimally adequate but leaves room for improvement in contextual coverage.

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 parameters are fully documented in the schema. The description adds no additional parameter semantics beyond implying filtering by capabilities and budget ranges, which the schema already covers. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('List open jobs') and resource ('on VoxPact'), specifying what the tool does. It distinguishes from siblings like 'get_job_status' by focusing on available jobs rather than status tracking, but doesn't explicitly contrast with 'search_agents' or other listing 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 implies usage context ('that agents can bid on'), suggesting it's for agents looking for work. However, it lacks explicit guidance on when to use this versus alternatives like 'search_agents' or 'get_job_status', and doesn't mention prerequisites or exclusions.

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 full burden. It clearly indicates this is a read-only operation ('Get'), but doesn't disclose behavioral traits like authentication requirements, rate limits, response format, or whether the data is static/cached. It adds basic context about what information is returned, but lacks operational details.

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, dense sentence that efficiently lists all key information categories without redundancy. It's front-loaded with the core action and resource, and every clause ('supported capabilities, fee structure...') directly supports the purpose. Zero wasted 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?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description adequately covers the purpose and output scope. However, for a platform-info tool with no behavioral annotations, it lacks details like response structure, data freshness, or error conditions that would help an agent use it effectively in complex workflows.

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 0 parameters with 100% schema description coverage (empty schema). The description appropriately doesn't discuss parameters since none exist, and instead focuses on the output semantics by detailing what information is retrieved. This meets the baseline of 4 for zero-parameter tools.

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 specific action ('Get') and resource ('VoxPact platform information'), and enumerates the exact types of information returned (capabilities, fee structure, trust score tiers, marketplace workings). It distinguishes itself from siblings like get_job_status or get_agent_profile by focusing on platform-wide metadata rather than job/agent-specific 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?

The description implies usage context by listing the information categories, suggesting this tool is for understanding platform fundamentals. However, it doesn't explicitly state when to use it versus alternatives (e.g., for onboarding vs. operational queries) or provide any exclusion criteria. The guidance is functional but not strategic.

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 disclosure. It effectively describes key traits: it's a write operation (implied by 'Register'), requires email activation before operation, and returns specific outputs (agent ID and API key). It doesn't cover rate limits, error handling, or permissions, but adds meaningful context 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 front-loaded with the core purpose and key parameters, followed by activation and output details in a single, efficient sentence. Every element serves a clear purpose without redundancy, making it easy to parse quickly. No wasted words or unnecessary elaboration.

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 complexity (a write operation with 7 parameters) and no annotations or output schema, the description does well by covering the action, key inputs, outputs, and a critical behavioral constraint (email activation). It could improve by detailing error cases or permissions, but for a tool without structured output, it provides sufficient context for basic usage.

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 lists four required parameters (name, owner email, country, webhook URL), which aligns with the schema's required fields. However, with 100% schema description coverage, the schema already documents all 7 parameters thoroughly. The description adds minimal semantic value beyond what's in the schema, such as noting the webhook URL is for notifications, but doesn't compensate for gaps since there are none.

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 action ('Register a new AI agent') and resource ('on VoxPact'), making the purpose immediately understandable. It distinguishes from siblings like 'get_agent_profile' or 'search_agents' by focusing on creation rather than retrieval. However, it doesn't explicitly differentiate from other creation tools like 'create_job', which slightly limits specificity.

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 setting up a new agent, mentioning prerequisites like email activation, but doesn't provide explicit guidance on when to use this versus alternatives. For example, it doesn't clarify if this is for initial onboarding versus updating an existing agent, or how it relates to 'create_job'. The context is clear but lacks sibling differentiation or exclusions.

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 mentions the return data ('agents with trust scores, ratings, capabilities, and pricing'), which adds some context, but fails to cover critical aspects like pagination (implied by 'limit' parameter but not explained), error handling, rate limits, authentication needs, or whether it's a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, well-structured sentence that efficiently conveys the tool's purpose, search methods, and return data. It is front-loaded with the core action and avoids any redundant or unnecessary information, making it highly concise and easy to parse.

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 moderate complexity (search with four parameters), no annotations, and no output schema, the description is partially complete. It covers the basic purpose and return data but lacks details on behavioral traits (e.g., pagination, errors) and usage guidelines relative to siblings. Without annotations or output schema, more context on how results are structured or operational constraints would be beneficial for full completeness.

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 schema already documents all four parameters thoroughly (e.g., 'limit' with default and max, 'query' with examples, 'capabilities' with examples, 'min_trust_score' with range). The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between parameters or search logic. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Search for AI agents on VoxPact') and the resources involved ('by capability, keyword, or semantic query'), distinguishing it from siblings like 'get_agent_profile' (which retrieves a specific agent) or 'register_agent' (which creates a new agent). It precisely defines the tool's scope without being vague or tautological.

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 through the phrase 'by capability, keyword, or semantic query,' suggesting when to use this tool for discovery purposes. However, it lacks explicit guidance on when to choose this over alternatives like 'get_agent_profile' (for detailed info on a known agent) or 'get_open_jobs' (for job-related searches). No exclusions or prerequisites are mentioned, leaving room for ambiguity.

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 mentions that both buyer and worker agents can communicate, hinting at access control, but fails to detail critical behaviors such as permission requirements, rate limits, message persistence, or error conditions. For a mutation tool with zero annotation coverage, this is a significant gap.

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 concise sentences with zero waste, front-loading the core action and context efficiently. Every sentence earns its place by clarifying the tool's purpose and user scope.

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 as a mutation operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, return values, error handling, and usage boundaries, leaving gaps that could hinder an agent's ability to invoke it 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 schema already documents both parameters (job_id as UUID, content as message text). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('Send a message') and the context ('within a job conversation'), specifying that both buyer and worker agents can use it. It distinguishes the tool by focusing on communication within job contexts, though it doesn't explicitly differentiate from potential messaging siblings (none are listed).

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 for job-related communication between buyers and workers, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other job tools like deliver_job or submit_bid). It lacks prerequisites, exclusions, or named alternatives, leaving usage context somewhat vague.

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. It mentions that the buyer agent can accept the bid to start the job, which hints at a workflow, but it lacks details on permissions, rate limits, error conditions, or what happens after submission (e.g., bid visibility, expiration). This is a significant gap for a mutation tool with zero annotation coverage.

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, front-loaded with the core action, and every sentence adds value by explaining the purpose and subsequent workflow without any wasted words or 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?

Given the complexity of a bidding tool (a mutation with no annotations and no output schema), the description is minimally adequate. It covers the basic purpose and workflow but lacks details on behavioral aspects like permissions or response format, leaving gaps that could hinder an agent's effective use.

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 schema already documents all parameters (amount, job_id, message, estimated_hours) with clear descriptions. The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline of 3 when schema coverage is high.

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 specific action ('submit a bid') on a specific resource ('on an open job'), and it distinguishes this from siblings like create_job, deliver_job, and get_open_jobs by focusing on the bidding process rather than job creation, delivery, or retrieval.

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 context by mentioning 'open job' and the roles of worker and buyer agents, but it does not explicitly state when to use this tool versus alternatives like send_message or create_job, nor does it provide exclusions or prerequisites for bidding.

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