value-pro-mcp

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

With no annotations, the description reveals the tool is deterministic and non-AI, which is important. But it omits details on side effects (likely none), read-only nature, and behavior when no answer is found.

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?

Single, dense sentence with no waste. All information is front-loaded and 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?

Tool is simple, but without an output schema, the description should indicate return format (e.g., text, confidence score). It only says 'answer' generically, leaving uncertainty about what the agent receives.

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 already has descriptions for both parameters (query and top_k). The description adds topic context but does not significantly enhance parameter understanding beyond what the schema provides.

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 clearly states 'deterministic search for an answer from FAQ database' with specific topics (cost, documents, deadlines, etc.) and explicitly contrasts with AI generation, distinguishing it from sibling tools like ask_assistant.

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 use for FAQ retrieval without AI, providing clear context. However, it does not explicitly state when not to use it or name alternative tools, leaving some 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?

With no annotations, the description carries full burden. It discloses the answer is preliminary, undergoes verification, exact conditions require operator confirmation, and the tool may be disabled. This is good transparency for a query 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?

Two concise sentences: first states the primary action and outcome, second adds scope and caveats. No redundant words, front-loaded effectively.

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 covers purpose, scope, output nature (advisory, verified), and operational status (may be disabled). Complete and sufficient.

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% with a single 'question' parameter described. The description adds context that the question should be within the domain of property valuation, which adds semantic value beyond the schema's minimal 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 the tool asks a question to the domain AI assistant VALPRO and returns a preliminary advisory answer specifically about property valuation. It distinguishes from application processing but does not explicitly differentiate from sibling tools like answer_faq or search_knowledge.

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 guidance: use for property valuation inquiries only, not for application submissions. This helps the agent decide when to use this tool, though alternatives are not named.

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 discloses that the calculation is preliminary, final price is confirmed by operator, and that certain parameters (purpose) don't affect price but affect documents. No destructive side effects or inconsistencies are mentioned, which is appropriate for a read-like calculation.

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 with focused content, covering key points efficiently. It is not overly verbose, though it could be slightly more structured by separating clauses. 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?

The tool has 8 parameters, 1 required, and no output schema. The description covers the pricing logic well but does not explain the output format (e.g., returns a number or object). Given the lack of output schema, a mention of return value would improve 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?

All 8 parameters have schema descriptions, achieving 100% coverage. The description adds significant value by explaining how parameters combine (e.g., visit included for damage, loyalty discount applied). It provides pricing logic that the schema alone does not convey.

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 calculates preliminary cost of an appraisal report, specifying components like base, visit, delivery, copies, and loyalty discount. It uses a specific verb ('считает') and resource ('стоимость отчёта об оценке'), and distinguishes from siblings like list_services which lists service IDs.

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 usage context (e.g., for preliminary cost, with special cases for damage types). It implies when to use (before creating a lead) but does not explicitly state when not to use or mention alternative tools. However, the context is sufficient for most agents.

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 provided, so the description carries the full burden. It discloses the no-PD constraint and return of links but does not explain side effects, idempotency, or other behavioral traits.

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 concise three-sentence paragraph that front-loads the core action and key constraint. No redundant phrases.

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 9-parameter tool with no output schema, the description covers the high-level purpose and no-PD constraint but does not describe return structure or prerequisites like needing service_id from list_services.

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 already well-described. The description adds minimal extra value beyond noting that parameters are same as calculation plus optional source_id.

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 records a preliminary application without personal data and returns links for processing. It distinguishes itself from sibling tools like calculate_price and list_services by emphasizing the no-PD soft handoff.

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 recording leads without personal data but lacks explicit when-to-use or when-not-to-use compared to siblings. No alternative tools are mentioned.

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 full burden. It mentions the tool returns data but does not disclose behavioral traits like read-only status, pagination, or authentication needs. It is adequate but lacks depth.

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 long, front-loads the key purpose, and contains no unnecessary words. It is maximally concise while conveying essential 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 simple list tool with no output schema, the description adequately states what is returned (prices, deadlines, purposes) and filtering. However, it lacks details on the return structure, such as field names, which would be needed by 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 coverage is 100% with a description for the parameter. The tool description adds 'Can be filtered by object category' which mirrors the schema. No additional semantics beyond the schema are provided.

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 returns a catalog of appraisal services with base prices, deadlines, and allowed purposes, and mentions filtering by category. This distinguishes it from sibling tools like calculate_price or search_knowledge.

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 does not explicitly state when to use this tool versus alternatives. It implies usage for browsing services, but lacks guidance on when not to use it or when to use siblings like calculate_price.

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 weight for behavioral disclosure. It only states the return of a document list, without mentioning safety, side effects (it appears read-only), error handling, or other behavioral traits that could affect agent decision-making.

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 immediately states the tool's purpose. It includes a useful example without extraneous information, making it 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?

For a simple tool with two well-defined parameters and no output schema, the description is adequate but incomplete. It does not hint at the output format (e.g., list of names or objects), which would help an agent anticipate the result 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?

Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning by giving an example ('apartment for court') that contextualizes the purpose parameter, but does not elaborate on enum values or parameter relationships beyond what the schema already provides.

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 ('Returns a list of documents') and the resource ('for a specific service and assessment purpose') with an example ('apartment for court'). It distinguishes this tool from siblings like list_services (which lists services) by specifying it provides documents for a given service and purpose.

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 offers no guidance on when to use this tool versus alternatives like ask_assistant or search_knowledge. It does not mention any prerequisites, limitations, or appropriate contexts beyond the basic functionality.

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 fully disclose behavior. It states verbatim facts and no AI generation, which is helpful. However, it does not mention authentication needs, rate limits, error handling, or side effects, leaving gaps in transparency.

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 directly state what the tool does and how to use its output. No fluff, front-loaded with key information. 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?

The tool has no output schema, so the description should elaborate on return format. It mentions 'fragments' but not structure (e.g., list of strings, objects, relevance). Sibling differentiation is weak. Adequate but not complete given the absence of an 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%, so baseline is 3. The description adds context about the type of content returned but does not provide additional semantics beyond the schema's parameter descriptions. For example, top_k is straightforward; query is sufficiently described.

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 returns verified factual fragments verbatim about the company, services, documents, etc. The verb 'Возвращает' and the list of topics specify the resource. However, it does not explicitly differentiate from sibling tools like answer_faq or ask_assistant, which may overlap in function.

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 retrieving factual information to formulate responses, but it lacks explicit guidance on when to use this tool versus alternatives. Sibling tools are listed but no exclusions or when-not-to scenarios are provided.

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