read_feedRead a curated finance & markets feed by its id (from list_feeds). Returns normalized items (title, link, published, summary). Optionally filter items by keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| feed | Yes | Curated feed id (from list_feeds). | |
| limit | No | Max items (1-50, default 20). | |
| query | No | Keyword filter over item title/summary. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "feed": "bloomberg-markets"
+ },
+ {
+ "feed": "reuters-finance",
+ "limit": 10,
+ "query": "earnings"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond the annotations by disclosing the output format ('normalized items (title, link, published, summary)') and the filtering capability. This complements the readOnlyHint/idempotentHint annotations, which already cover safe, read-only behavior. However, it does not mention potential error conditions or rate limits, so it stops short of a perfect score.
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 only two sentences, with the core action front-loaded and every clause earning its place. It efficiently conveys the purpose, required input, return format, and an optional filter without 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's simplicity, the description is fairly complete. It covers the prerequisite (list_feeds), return fields, and optional filter. It does not explicitly mention limit behavior (though the schema documents it) or detail what 'normalized' means, but for a read-only feed tool with solid annotations, the context is 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 description coverage is 100%, so the baseline is 3. The description adds minimal extra meaning beyond the schema: it clarifies the feed is 'curated finance & markets' and that query filters over title/summary, but these are mostly echoed in the schema property descriptions. It does not significantly enhance parameter understanding.
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 reads a curated finance & markets feed by id and returns normalized items, with a specific verb and resource. It references list_feeds for id lookup, but does not explicitly distinguish itself from the sibling tool fetch_feed, so it misses the top mark for sibling differentiation.
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 context on how to use the tool: it requires a feed id from list_feeds, and mentions optional keyword filtering. It does not explicitly state when not to use it or name alternatives, but the prerequisite from list_feeds gives sufficient usage guidance.
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.
Most tools have distinct purposes, but there is overlap among query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and several Polymarket-specific tools. Descriptions help differentiate, but some confusion is possible.
All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, compare_entities, validate_claim). No mixing of conventions, and naming is predictable.
33 tools is a high count for a finance server, including many meta-tools (memory, subscriptions, feedback) that are not finance-specific. The core finance set is reasonable, but the overall surface feels heavy.
The server covers many data sources (SEC, FRED, FDA, etc.) but lacks direct stock quotes or fundamental CRUD operations. Some areas (e.g., Polymarket, AI visibility) are over-represented, leaving gaps in core finance tasks.