Search profiles with the Linked Panda LinkedIn shape using bounded public Wikimedia data; unavailable LinkedIn fields are explicitly null. $0.30/call via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| geoId | No | ||
| title | No | ||
| school | No | ||
| search | No | ||
| lastName | No | ||
| location | No | ||
| firstName | No | ||
| followerOf | No | ||
| industryId | No | ||
| pastCompany | No | ||
| currentCompany | No | ||
| requestContext | No |
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?
With no annotations, the description carries the disclosure burden and does disclose several actionable behaviors: the data source is bounded, unavailable LinkedIn fields are explicitly null, and each call costs $0.30 via x402. It does not mention pagination, result envelope, or empty-result behavior, but the cost and null-field disclosures are especially valuable for an agent deciding whether to call.
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?
A single dense sentence contains no filler; each clause adds a distinct fact: the action, result shape, data source, null-field behavior, and per-call cost. It is front-loaded with the verb and resource, making it easy for an agent to absorb quickly.
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?
The tool is moderately complex: 13 parameters, no output schema, no annotations. The description covers source, cost, and output nullity, but omits pagination behavior, result fields beyond 'LinkedIn shape', and any guidance on how to construct a search among the many filters. An agent would likely need a test call or external doc to use it confidently.
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 0%, and the description adds no parameter-level context, only the general point about null LinkedIn fields. Several parameter names are self-explanatory (firstName, currentCompany, school), but others like geoId, industryId, and requestContext remain unexplained. With 13 optional parameters and no guidance on combinations or required filters, the description does not compensate for the schema's lack of detail.
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?
Description states a precise verb ('Search profiles') and a clear resource ('Linked Panda LinkedIn shape' over 'bounded public Wikimedia data'), which distinguishes it from generic profile-search siblings like rep_stableenrich_search. The mention that unavailable LinkedIn fields are null further defines the output shape. Despite the specialized 'Linked Panda' label, the core purpose is unambiguous.
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 the use case: profile searches that want LinkedIn-shaped results from Wikimedia-derived data, tolerating null fields. However, it does not explicitly state when not to use it, compare with rep_stableenrich_search or pdl_people_enrich, or provide filter-selection guidance. The context is implied rather than spelled out.
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.
Several tool groups are nearly indistinguishable: wallet_analyze, wallet_spy, and base_wallet_profile all inspect wallets; batch_extract, batch_url_json, and x401_batch_extract all batch-extract URLs; route_task, agentcore_route, and mpp_route all perform routing. An agent would need to read very carefully to avoid selecting the wrong tool.
All names are snake_case, but there is no consistent verb_noun or namespace pattern: many are noun-only (inference, echo, sentiment, server_time), some are prefixed by domain (bazaar_, base_, x402_, rep_), and action prefixes vary widely (fetch_, compile_, extract_, purchase_, route_). The naming is readable but not predictable across the set.
Seventy tools is an extremely large surface for an agent to choose from, and most appear to be independent paid service wrappers. This exceeds the 50+ extreme mismatch threshold in the calibration and creates an overwhelming selection problem.
Relative to its apparent purpose—exposing x402 payments and Bazaar market data—the coverage is extensive: diagnostics, preflight, settlement verification, receipt lookup, wallet checks, Bazaar analytics, web extraction, and text processing are all represented. The main gaps are operational side-effects like creating or updating a Bazaar listing, but those appear to be outside this read/purchase surface.