mcp-server

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

Annotations already indicate read-only and non-destructive behavior. The description adds value by outlining the types of data returned (events, sentiment, signals, risk factors), beyond what annotations provide. No contradictions noted.

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. The purpose is front-loaded, and every word contributes meaning. 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?

Given the presence of an output schema, the description adequately covers the tool's purpose and output categories. For a simple two-parameter tool, this is sufficient, though it could briefly note that it works for any stock symbol.

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% coverage with clear descriptions for both parameters (symbol, days). The description adds no further parameter semantics, so baseline score 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 specifies 'Position health check for a stock' with a clear verb and resource. It lists distinct return categories (material events, sentiment, technical signals, risk factors), which differentiates it from siblings like 'pulse' or 'institutional' that focus on narrower aspects.

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 comprehensive stock assessment but does not explicitly state when to use this tool versus alternatives like 'pulse' or 'investigate'. No exclusions or prerequisites 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the tool is clearly safe. The description adds no behavioral context beyond query types (e.g., no mention of data freshness, pagination, or rate limits). This is adequate but not enriched.

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: one sentence stating purpose, followed by a bullet list of query types. Every sentence adds value, and critical information is 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?

Given the tool has 4 parameters (all well-documented) and an output schema, the description provides adequate context through query types. It could explicitly state that certain queries require an identifier, but the schema already notes this. Minor gap: no mention of required vs optional parameters.

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 detailed parameter descriptions (e.g., identifier: 'Symbol or manager name/CIK'). The description repeats query types but adds no meaning beyond the schema. Baseline 3 due to high schema 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 the tool provides 'Institutional investor intelligence from 13F filings' and lists specific query types (manager, security, signal). This distinguishes it from sibling tools that are more generic (assess, investigate, etc.).

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 lists three query types but does not explicitly guide when to use each or advise against using this tool for non-institutional queries. Usage is implied by the query types, but no exclusions or alternatives are 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?

Annotations already indicate read-only and non-destructive. The description adds significant behavioral context: auto-detection of entity type from subject format (symbol/CIK/sector name) with examples, which is valuable beyond annotations.

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?

Extremely concise, front-loaded with purpose. All sentences are informative: one for overview, then list of auto-detection rules. No redundancy or 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?

Given output schema exists, return values are covered. The description explains input constraints and auto-detection. Missing error handling for invalid subjects, but overall adequate for a simple lookup tool.

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 enhances parameter semantics with concrete examples for the subject parameter (AAPL, CIK, Technology), adding value over schema 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 clearly states it researches entities (company, insider, sector) with specific verb 'Research' and resource types. It distinguishes from siblings like 'screen' or 'search' by focusing on entity lookup, though not explicitly contrasting.

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 entity research and provides auto-detection logic, giving guidance on how to specify subjects. However, it lacks explicit when-not or comparisons to sibling tools.

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

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

Annotations already declare readOnlyHint and destructiveHint. The description adds value by specifying the exact data returned and emphasizing no parameters, but does not disclose any potential latency or data freshness.

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 efficient sentences, front-loading the purpose and listing outputs, with no redundancy. Perfect size for a simple 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 simplicity (no params, output schema present), the description covers all necessary information: what it does, what it returns, and how to call it with an example.

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 an empty input schema, the description reinforces that no parameters are needed, which is helpful. Coverage is 100% so baseline is 3; the explicit 'No parameters needed' pushes it to 4.

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 returns a market snapshot with specific data types (movers, filings, trades, indicators), distinguishing it from siblings like 'assess' or 'screen' which are more focused.

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 a quick market overview by calling it 'what's happening right now' and includes an example, but does not explicitly contrast with alternative tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond that (e.g., what the scan returns, how scoring works, or any side effects).

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 sentence, which is concise, but it is too vague to be fully effective. Every sentence should earn its place; this one is minimal but not sufficiently informative.

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?

With 5 parameters and sibling tools, the description is too brief to provide complete context. Although output schema exists, the description lacks an overview of what the scan produces or when to use it.

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% (all 5 parameters documented). The description does not add any parameter details, but the schema itself provides adequate semantics for each parameter.

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 'Scan market for opportunities' states the verb and resource but is vague; it does not differentiate from sibling tools like 'search' or 'investigate'. The title 'Scan Opportunities' is similarly generic.

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?

No guidance on when to use this tool vs alternatives. No exclusions, prerequisites, or context for selection 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, assuring safe read-only behavior. The description adds that search is semantic and uses PostgreSQL, but does not disclose any rate limits, pagination behavior, or return format. It provides some additional context beyond annotations but is not comprehensive.

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 very concise with three short sentences. It front-loads the main purpose and uses no unnecessary words. Every sentence adds value, making it efficient for an AI agent 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 that an output schema exists, return values do not need elaboration. The description covers the tool's scope and use case. However, it could mention result ranking or pagination behavior to be fully complete. Still, it is mostly adequate for the tool's complexity.

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% coverage, so parameters are well-documented. The description adds only a minor detail ('Natural language supported' for the q parameter). Since the schema already provides sufficient meaning, the description adds limited value. 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 identifies the tool's purpose as semantic search across multiple entity types (filings, companies, insiders, managers) and mentions PostgreSQL full-text search. However, it does not explicitly differentiate from sibling tools like 'assess' or 'investigate', leaving some ambiguity about when to choose this over others.

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 states it is 'perfect for finding specific events, people, or companies,' giving a usage context. However, there is no guidance on when not to use it or any mention of alternatives among siblings. The guidance is adequate but lacks exclusions or comparisons.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds no additional behavioral traits (e.g., rate limits, data freshness) beyond what annotations provide, which is acceptable but minimal.

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 short sentence, very concise and front-loaded with the core purpose. It avoids redundancy, though it could be slightly more informative without losing 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 low complexity (one optional parameter, full schema coverage, presence of output schema), the description is complete enough. It tells the agent what the tool does, and the schema details the parameter. Only minor missing context about output schema purpose.

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 for the single parameter, including detailed explanations of each allowed value. The tool description itself does not add further semantic meaning beyond what the schema already provides, meeting 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 provides info about the Profitelligence service and the user's account. The input schema further refines this with specific info types (overview, profile, pricing, capabilities, status). It is distinct from siblings which have different focus areas.

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 its siblings (assess, institutional, etc.) or any prerequisites. It does not mention scenarios where it should or should not be used, leaving the agent to infer usage context.

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