scholarly_searchSearch Lens.org scholarly works (academic papers, preprints) by free-text or Lucene query (requires Bearer token); returns title, authors, journal, year, and DOI.
| Name | Required | Description | Default |
|---|---|---|---|
| from | No | ||
| size | No | ||
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "quantum computing"
+ },
+ {
+ "query": "author:\"Einstein\" title:relativity",
+ "size": 100
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Scholarly works search results from Lens.org API",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds authentication requirement and expected return fields beyond annotations (readOnlyHint, idempotentHint). No contradiction with 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?
Single sentence with clear structure, front-loads purpose. Uses semicolon effectively but could be more scannable.
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?
Covers return fields and basic behavior but fails to explain pagination parameters ('from', 'size') given schema coverage gap. Output schema partially mitigates return format documentation.
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 has no descriptions (0% coverage). Description explains 'query' as free-text or Lucene but omits 'from' and 'size' parameters, which are only hinted as numbers. Insufficient to guide proper parameter usage.
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?
Clearly states verb 'Search', resource 'Lens.org scholarly works', and specifies result fields (title, authors, journal, year, DOI). Differentiates from siblings like 'patent' and 'patents_search' by focusing on scholarly works.
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?
Mentions authentication requirement ('requires Bearer token') but offers no guidance on when to use this tool versus alternatives (e.g., 'scholarly' sibling). Does not specify constraints or best practices for query type selection.
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.