Audited SEC catalyst signals for agents — ranked filing intel with a public track record. Free tier.
Today's top scored catalyst picks from the Catalyst Edge convergence model. Optional filters: conviction, sector. Free tier returns the top 3 of the full board; founding access ($99 one-time, never renews) returns full result sets here and unlocks the 4 gated tools — see the upgrade field on any free response, or /.well-known/agent-card.json.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max picks (default 25; free tier capped at 3) | |
| sector | No | filter by sector (substring match) | |
| conviction | No | filter by conviction level |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It does well by revealing free-tier limits, paid access effects, the upgrade field, and gated tools. It stops short of describing response structure, rate limits, or failure behavior, but the access-related behavior is unusually transparent.
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 compact and front-loaded: purpose first, then filters, then access details. Both sentences earn their place, and no information is buried or redundant.
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 list tool with no output schema, the description explains what the agent gets, how access limits affect results, and how to discover upgrade options. It could be more explicit about the shape of a pick or the sort order, but the core context needed to call it correctly is present.
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 documents all three parameters with 100% coverage, so the baseline is 3. The description adds only that filters are optional and that the free tier caps the limit, which mostly restates what the schema already says.
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 a specific verb and resource: 'Today's top scored catalyst picks from the Catalyst Edge convergence model.' This clearly identifies what the tool returns and distinguishes it from the sibling tools, which target options context, sector lean, ticker signals, and track records.
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?
It provides clear context for when to use the tool: to retrieve top catalyst picks with optional conviction and sector filters. It also explains the free-tier and founding-access behavior, but it does not explicitly state when to prefer this tool over sibling tools or mention exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live options context for a ticker via Tradier — nearest expiration, ATM call/put prices, straddle cost. Use to choose an options strategy.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | ticker symbol |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It does indicate that the data is 'live' and sourced via Tradier, and it lists the returned fields. However, it doesn't mention error behavior, rate limits, or response structure. For a simple read-only data retrieval tool, this is adequate but not rich.
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 main purpose and specific data points. It contains no redundant information and is highly scannable, earning a perfect score.
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 fairly complete: it lists the returned data elements and the intended use case. However, it could explicitly describe the output format, so it doesn't reach a 5.
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 fully documents the only parameter 'ticker' with the description 'ticker symbol' (100% coverage). The description adds no additional meaning beyond what the schema provides, so the 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 clearly states the tool provides live options context for a ticker via Tradier, including nearest expiration, ATM call/put prices, and straddle cost. This distinguishes it from sibling tools like get_convergence_picks or get_ticker_signal, though it doesn't explicitly compare itself to them. The verb is implied by the name 'get' and the phrase 'Live options context' makes the retrieval purpose clear.
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 includes an explicit usage directive: 'Use to choose an options strategy.' This gives clear context for when to use the tool. However, it does not provide any exclusions or mention alternative tools, so it falls short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Orphan-aggregated sector lean — the directional signal each sector is showing, from ~480 aggregated data spokes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only mentions the data source (~480 aggregated data spokes) without explaining what that means. It does not state the tool's read-only nature, output structure, or any potential side effects. The term 'orphan-aggregated' adds confusion rather than clarity.
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 with no filler, front-loading the key concept 'sector lean'. However, the phrase 'orphan-aggregated' is unusual and not self-explanatory, which slightly reduces clarity. Overall, it is concise and appropriately sized.
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, no output schema, and zero parameters, the description carries the full burden of completeness. It does not explain what the output looks like, how to interpret the directional signal, or the meaning of 'orphan-aggregated'. An agent would have insufficient context to fully understand the tool's behavior and result.
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 zero parameters, so the baseline is 4. The description does not need to add parameter details, and the empty schema is sufficient. There are no parameters to explain.
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 identifies the resource as 'sector lean' and describes it as the directional signal each sector is showing, which goes beyond a tautology. However, 'orphan-aggregated' is vague jargon that obscures the tool's exact purpose, and the lack of an explicit verb makes the action less clear. It distinguishes from siblings by sector focus but only implicitly.
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 no guidance on when to use this tool versus alternatives like get_ticker_signal. It does not mention any prerequisites, conditions, or contexts. The only hint is the sector focus, which implies a use case but is not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Plain-language thesis for a ticker — quantitative read, catalysts, risks, bear case. Covers the top convergence picks.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | ticker symbol |
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 describes the content of the thesis but not the return structure, data freshness, limitations, or any operational context. This under-transparency leaves the agent guessing about what to expect.
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 is front-loaded with the core purpose. The trailing phrase about convergence picks is slightly ambiguous but does not add unnecessary length or waste.
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 one-parameter tool with no output schema, the description lists the key components of the thesis and gives a sense of scope. However, it omits how the response is structured, what 'top convergence picks' refers to, and any limitations, leaving gaps in 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?
The single 'ticker' parameter is fully documented in the schema (coverage 100%), so the baseline is appropriate. The description does not add any extra meaning or constraints to the parameter beyond what the schema already states.
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 ('thesis') scoped to a ticker, and enumerates the thesis components (quantitative read, catalysts, risks, bear case). This clearly differentiates it from sibling tools like get_ticker_signal or get_convergence_picks.
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 this tool is for a comprehensive, plain-language view of a ticker, but it does not explicitly state when to use it instead of siblings or provide alternative recommendations. The guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full convergence breakdown for one ticker — every non-zero signal layer and its points, plus the score and conviction.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | ticker symbol |
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 output behavior (non-zero layers, points, score, conviction) but does not explicitly mention read-only status, side effects, or error conditions. The read-only nature is implied by the name 'get', but not stated.
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, front-loaded sentence that is concise and informative. Every element contributes to understanding the tool's function, with 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 the simple one-parameter schema and lack of output schema, the description adequately explains the return values (layers, points, score, conviction). It is complete enough for an agent to understand what the tool produces, though it could be more explicit about output format or 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 description coverage is 100% with 'ticker symbol', and the description adds no additional meaning beyond referencing 'one ticker'. The baseline of 3 applies since the schema already documents the 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 clearly states the tool provides a full convergence breakdown for one ticker, enumerating signal layers, points, score, and conviction. It distinguishes itself from sibling tools like get_convergence_picks by explicitly focusing on a single ticker.
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 usage is implied by 'for one ticker' but there is no explicit guidance on when to use this versus alternatives like get_convergence_picks, nor any exclusions or prerequisites. It lacks clear contextual directions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Historical hit-rate and alpha of Catalyst Edge pick lists — the evidence an agent uses to weight the signal. Optional: list_name filter.
| Name | Required | Description | Default |
|---|---|---|---|
| list_name | No | filter to one pick list (substring match) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that data is historical and notes the optional list_name filter, but it does not describe return structure, sorting, pagination, or any side effects. It adds some context beyond the schema but leaves 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 two concise sentences, front-loaded with the core purpose and followed by the optional filter note. Every word earns its place; no fluff or redundant 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 single-parameter read tool without an output schema or annotations, the description covers the key elements: what data is returned, its purpose, and the available filter. It lacks an explicit return format or example, but the simplicity of the tool makes this 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% for the single parameter, list_name, which already includes a description ('filter to one pick list (substring match)') and optionality. The description only repeats that the filter is optional, adding no additional 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 description clearly identifies the resource: historical hit-rate and alpha of Catalyst Edge pick lists. It distinguishes the tool from siblings by focusing on track record rather than current picks or market context. However, it lacks an explicit verb like 'retrieves' or 'returns', instead using an implied verb from the tool 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 implied usage context ('the evidence an agent uses to weight the signal'), suggesting when the tool would be relevant. However, it does not explicitly contrast with sibling tools or state when not to use it, so alternatives and exclusions are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Each tool targets a distinct output: top picks, per-ticker signal breakdown, narrative thesis, options context, sector lean, and historical track record. The closest pair is get_ticker_signal and get_thesis, but one is explicitly quantitative layers and the other is plain-language narrative, so an agent should not confuse them.
All six tools follow a consistent get_<object> pattern with clear noun phrases. There are no mixed conventions, vague verbs, or inconsistent casing.
Six tools is a tight, well-scoped set for a read-only market-research MCP server. Every tool covers a distinct analytical need without redundancy or bloat.
The surface covers the full research workflow: discovering top picks, drilling into per-ticker signals, reading narrative theses, checking options context, understanding sector lean, and validating with historical track record. For a read-only analytics domain, there are no critical dead ends or missing lifecycle operations.
