Most complete single-call company intelligence: SEC 8-K filings + USASpending federal contracts + GDELT global news + product changelog + Stooq quote data. 5 unique sources.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | No | Stock ticker for finance data | |
| company | Yes | Company name e.g. 'Palantir', 'Anthropic' | |
| github_url | No | Optional GitHub repo or org URL for changelog accuracy |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful context by listing the exact sources (SEC, USASpending, GDELT, changelog, Stooq), but does not disclose behavioral traits like potential latency, result aggregation format, or failure modes, which is a minor gap for a multi-source 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?
The description is two sentences, front-loaded with the key value proposition, and lists sources compactly. Every word earns its place, with no redundancy or fluff.
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 (5 sources) and lack of output schema, the description should clarify what the caller receives—e.g., a synthesized landscape report, per-source JSON, or structured data. It currently only says 'company intelligence,' which is vague. The source list helps, but the return shape and how to interpret results remain unclear.
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 already documented. The description does not add any param-specific meaning—it does not clarify which sources depend on ticker vs company vs github_url, leaving the existing schema descriptions as the sole source.
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 as single-call company intelligence and lists five specific data sources, distinguishing it from sibling tools like extract_finance_landscape or extract_gov_landscape. However, it lacks an explicit action verb like 'extracts' or 'retrieves', relying on the tool name for that.
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 phrase 'Most complete single-call company intelligence' provides clear context for when to use this tool (when broad multi-source intelligence is needed in one call). It implicitly differentiates from the individual category tools by enumerating the combined sources, but does not explicitly state when to choose an alternative or mention exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct data source (finance, GitHub, Hacker News, etc.), with clear separation and no overlap. An agent can easily distinguish which tool to use for a given source.
Tools use a consistent verb_noun pattern with 'extract_' for data extraction and 'search_' for search functions. The outlier 'package_trends' is still descriptive and fits the theme, so the pattern is mostly predictable.
11 tools is well-scoped for a data aggregation server. Each tool serves a clear purpose and the count is neither too sparse nor overwhelming.
The server covers a broad range of sources (finance, code, news, social, academia, jobs, packages). Minor gaps like missing Twitter or general news are acceptable given the breadth already provided.