affiliationFuzzy affiliation-string match. Returns candidates with confidence scores.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| items | No | Matching organizations ranked by confidence | |
| time_taken | No | API response time in milliseconds | |
| number_of_results | No | Total number of matching candidates |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "text": "Department of Computer Science, MIT, Cambridge, MA"
+ },
+ {
+ "text": "University of Oxford, UK"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Fuzzy affiliation matching results with confidence scores",
+ "properties": {
+ "items": {
+ "description": "Matching organizations ranked by confidence",
+ "items": {
+ "properties": {
+ "country": {
+ "properties": {
+ "country_code": {
+ "type": "string"
+ },
+ "country_name": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "id": {
+ "description": "ROR organization ID (URL)",
+ "type": "string"
+ },
+ "name": {
+ "description": "Organization name",
+ "type": "string"
+ },
+ "score": {
+ "description": "Confidence score (0-1)",
+ "type": "number"
+ },
+ "types": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "number_of_results": {
+ "description": "Total number of matching candidates",
+ "type": "number"
+ },
+ "time_taken": {
+ "description": "API response time in milliseconds",
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds context about the fuzzy nature of the match and the return of candidates with confidence scores, which is useful. However, it does not disclose any limitations or specifics about the matching algorithm, but the strong annotations reduce the burden on the description.
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 extremely concise, consisting of two short sentences that front-load the core purpose and outcome. There is no fluff or redundancy; every word contributes to the description's value.
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 (one parameter, output schema present) and strong annotations, the description covers the basic purpose and a hint of the return format. However, it lacks usage context, such as when to choose this tool over alternatives and what constitutes a valid affiliation string. It is adequate for a straightforward tool but not fully complete.
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 coverage is 0%, so the description must compensate. It mentions 'affiliation-string' which implies the 'text' parameter is an affiliation string, but this is minimal and largely tautological given the tool name. It does not elaborate on input format, examples, or accepted variations, leaving the agent with little additional semantic insight.
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 a specific verb ('match') and resource ('affiliation-string'), indicating the tool performs fuzzy matching on affiliation strings and returns candidates with confidence scores. It distinguishes itself from generic search or entity resolution tools, though it could more explicitly differentiate from sibling tools like resolve_entity.
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?
No guidance is provided on when to use this tool versus alternatives. The description only states what it does, not when it should be invoked, prerequisites, or exclusions. This is a significant gap given the array of similar sibling tools.
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 tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps with ask_pipeworx, and discover_tools/suggest_questions both serve a discovery role. The extremely detailed descriptions help, but the boundaries between meta-tools and research tools are genuinely confusing, especially with 34 tools in one namespace.
Naming is a mix of bare verbs (get, search, recall), nouns (affiliation, entity_profile, recent_changes), and verb_noun phrases (resolve_entity, compare_entities, validate_claim). Some families are consistent (polymarket_*), but overall there is no uniform convention or prefix scheme, making the set feel arbitrary.
34 tools is well above the 25+ threshold for 'too many.' While the broad data-platform scope explains some of the count, many tools are meta-utilities (feedback, trending, memory, subscription management) and there are near-duplicate variants (three ask_pipeworx forms, six Polymarket tools) that inflate the surface.
For a data-research platform the surface is impressively complete: lookup, grounded verification, entity profiles, comparisons, claim checking, subscription lifecycle, memory, and tool discovery are all covered. Minor gaps exist (e.g., no direct general-purpose web fetch, and ROR lacks create/update, which is acceptable for a curated registry), but agents should rarely hit dead ends.