patentFetch a single Lens.org patent record by its lens_id (requires Bearer token); returns full bibliographic data including claims, inventors, and priority dates.
| Name | Required | Description | Default |
|---|---|---|---|
| lens_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "lens_id": "US10123456B2"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Single patent record from Lens.org API",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds authentication requirements (Bearer token) and specifics about returned data (claims, inventors, priority dates), which are not in 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?
The description is a single, well-structured sentence (via semicolon) that conveys purpose, required parameter, authentication, and output. Every part is essential, with no wasted 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 single parameter, rich annotations, and presence of an output schema, the description adequately covers purpose, auth, and return fields. It lacks mention of behavior when the record is not found, which is a minor gap.
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 0% schema description coverage, the description must compensate. It only mentions 'by its lens_id' without explaining the format or what lens_id represents. The example 'US10123456B2' is in the schema but not elaborated in the description.
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 'Fetch a single Lens.org patent record by its lens_id', specifying the verb, resource, and unique identifier. It distinguishes from the sibling 'patents_search' tool by targeting a single record.
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 indicates it fetches a single record by ID and requires a Bearer token, giving clear context. It does not explicitly exclude usage for search or mention alternatives, but the contrast with 'patents_search' is implied.
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.
Multiple tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are three variants of the same router (beta is currently identical), while bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. ai_visibility_check is effectively a single-entity version of scan_competitor_ai_presence, and discover_tools overlaps heavily with suggest_questions.
Tool names are uniformly snake_case and descriptive, but the pattern is mixed: verb-first names (ask_pipeworx, compare_entities, search_within) coexist with noun-first names (patent, scholarly, entity_profile), and the Lens.org pairing of patent/patents_search vs scholarly/scholarly_search is structurally inconsistent. Subgroups like pipeworx_* and polymarket_* are internally consistent, keeping the overall set readable.
At 35 tools, this exceeds the 16-25 'heavy' band and bundles at least four distinct domains: Lens.org bibliometrics, Pipeworx data routing, Polymarket trading analysis, and memory/subscription utilities. While many tools serve legitimate purposes, the set feels sprawling and several variants (e.g., the three ask_pipeworx flavors) inflate the count without adding equivalent value.
The Pipeworx/Polymarket ecosystem is thoroughly covered with routing, grounded answers, deep research, entity resolution, validation, comparison, monitoring, and memory all present. The Lens.org portion is thin (search + fetch for patents and scholarly works) but covers the core read path; minor gaps exist such as no batch/export operations and no patent-number lookup.