catalyst-edge-mcp

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

With no annotations, the description carries the full burden. It discloses the free tier cap (limit 3) and optional filters, but lacks details on authentication, rate limits, or return behavior beyond that. Some behavioral context is added but 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?

Two sentences with no wasted words. The first sentence states purpose, the second adds optional filters and constraints. Highly concise and 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 no output schema, the description does not explain return format or fields. It covers core purpose and parameter constraints but leaves out details like order of results or behavior when no matches found. Adequate but not thorough.

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 value by noting the free tier limit affecting the limit parameter and clarifying that conviction filter uses specific levels. This goes beyond 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 retrieving top scored catalyst picks from a specific model, with optional filters. The verb 'get' and resource 'convergence picks' are specific, and the tool is distinct from siblings like get_options_context or get_ticker_signal.

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 obtaining top picks with optional filters, but provides no explicit guidance on when to use this tool over alternatives or when not to use it. Usage context is implied but not elaborated.

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 it provides live data via Tradier and lists returned fields, but does not mention read-only status, authentication requirements, or potential errors. This is adequate but lacks safety cues.

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 concise sentences, front-loaded with the primary purpose, followed by specific data examples and a use case. No unnecessary words, every sentence adds value.

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 simple tool with one parameter and no output schema, the description adequately covers what the tool returns and when to use it. It could briefly mention return format, but the listed fields suffice.

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 the single parameter 'ticker' with description 'ticker symbol' (100% coverage). The description adds no additional meaning beyond mentioning 'for a ticker', so it does not improve on the schema. 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 clearly states the tool provides live options context for a ticker, including nearest expiration, ATM call/put prices, and straddle cost. It distinguishes from sibling tools by specifying options-specific data rather than convergence picks or sector lean. However, it lacks a strong verb-resource construction like 'Retrieve options context'.

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 ends with 'Use to choose an options strategy,' giving a clear use case. However, it does not provide when-not-to-use guidance, prerequisites, or alternative tools, leaving the agent to infer from sibling names.

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 should disclose behavioral traits. It mentions data aggregation from ~480 spokes but does not describe side effects, permissions, or output behavior such as pagination or rate limits.

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 concise sentence with a dash for emphasis. No wasted words, though 'orphan-aggregated' may confuse some users.

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 zero parameters, no output schema, and no annotations, the description should clarify the return value. It mentions directional signal per sector but does not specify the data structure (e.g., list, object, or map). Some improvement is needed to fully inform the 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?

There are zero parameters, and the schema coverage is 100%, so the description is not required to explain parameters. Baseline is 4, and the description adds no irrelevant param info.

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 'sector lean' as the resource and 'aggregated directional signal' as the output, which clarifies the purpose beyond the tool name. However, the phrase 'orphan-aggregated' is jargon and not immediately clear, and it doesn't explicitly distinguish from sibling tools like get_ticker_signal.

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 is provided on when to use this tool versus alternatives. The description lacks any when-to-use or when-not-to-use criteria, and does not mention 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?

No annotations are provided, so the description must disclose behavioral traits. It mentions what the thesis covers but omits details on side effects, authorization needs, rate limits, or error handling. The tool is likely read-only, but this is not confirmed.

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 that efficiently communicates purpose and content. It front-loads the core function ('Plain-language thesis for a ticker') and lists key components. The phrase 'Covers the top convergence picks' is somewhat vague but does not detract significantly.

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 (one parameter, no output schema), the description is partially complete: it lists what the thesis includes but does not explain the return format or how 'top convergence picks' relates. Leaves some ambiguity about whether the output is for a single ticker or a set of picks.

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 3. The description does not add extra meaning to the ticker parameter beyond what the schema's description ('ticker symbol') 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 tool provides a plain-language thesis for a ticker, listing components like quantitative read, catalysts, risks, and bear case. It distinguishes from siblings by focusing on thesis generation for a specific ticker, though the mention of 'top convergence picks' introduces slight 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?

No explicit guidance on when to use this tool versus siblings like get_convergence_picks or get_ticker_signal. The description implies it's for in-depth analysis of a single ticker but doesn't state prerequisites or when not to use it.

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

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

No annotations exist, so the description carries full burden. It describes the output contents but does not disclose behavioral traits such as authentication requirements, rate limits, side effects (likely none), or error handling.

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, concise sentence that front-loads the purpose (full breakdown) and lists key output elements. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool is simple with one parameter and no output schema, the description is fairly complete in explaining what the tool returns. It could mention output format or constraints for completeness, but covers the essential functionality.

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 minimal description 'ticker symbol'. The main description adds no extra semantic meaning beyond confirming it is for one ticker. 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 tool provides a full convergence breakdown for one ticker, including all non-zero signal layers, points, score, and conviction. It distinguishes from siblings like get_convergence_picks which likely handles multiple tickers.

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 single ticker's detailed breakdown but does not explicitly state when to use this tool versus alternatives like get_convergence_picks or get_options_context. 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?

With no annotations, the description must fully disclose behavioral traits. It states it returns historical data, but does not mention read-only nature, auth requirements, data recency, or any side effects. The agent is left unaware of important constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences: the first states the main purpose, the second states the optional filter. Every word contributes value, and it 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 lack of output schema, the description should at least hint at the return structure (e.g., per-list metrics, format). It does not, leaving the agent uncertain about what the tool returns. The single parameter is well-covered, but overall completeness is average.

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 the schema already describing 'list_name' as a substring match filter. The description merely echoes that it's optional, adding no additional meaning beyond the schema. 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 clearly states the tool returns 'Historical hit-rate and alpha of Catalyst Edge pick lists' and mentions it's evidence for weighting signals. This distinguishes it from siblings like get_convergence_picks (which likely returns picks) or get_sector_lean. However, the phrase 'the evidence an agent uses to weight the signal' is a bit vague.

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_convergence_picks or get_ticker_signal. It only mentions an optional filter but does not specify use cases or when not to use it.

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