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Glama

Bis

by io.github.pipeworx-io

Search Dataflows

search_dataflows
Read-onlyIdempotent

Search the BIS SDMX dataflow registry by keyword against dataflow name and ID. Returns matched flow_refs (e.g., 'BIS,WS_CBPOL_D,1.0') ready to pass to fetch_dataset, capped at limit (default 25, max 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 25, max 100)
queryYesKeyword (matches dataflow name)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
returnedYesNumber of flows in this response
dataflowsYesMatching dataflow objects
total_matchedYesTotal flows matching query

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "policy rates"
+  },
+  {
+    "limit": 50,
+    "query": "exchange rates"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "dataflows": {
+      "description": "Matching dataflow objects",
+      "items": {
+        "properties": {
+          "flow_ref": {
+            "description": "SDMX dataflow reference",
+            "type": "string"
+          },
+          "id": {
+            "description": "Dataflow ID",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "name": {
+            "description": "Dataflow name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "version": {
+            "description": "Dataflow version",
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "flow_ref",
+          "id",
+          "version",
+          "name"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "returned": {
+      "description": "Number of flows in this response",
+      "type": "number"
+    },
+    "total_matched": {
+      "description": "Total flows matching query",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_matched",
+    "returned",
+    "dataflows"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds useful behavioral details beyond annotations: keyword matching against both name and ID, the capped result size with default and maximum limits, and the return format with a concrete example. This provides sufficient transparency for a read-only search operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences, front-loaded with the action and resource. The example of a flow_ref is compact and illustrative. No redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only 2 parameters, 100% schema coverage, and an output schema present, the description provides all necessary context: what it searches, how results are capped, and how results integrate with a sibling tool. The description is complete for a search-then-fetch workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already describes both parameters at 100% coverage, so baseline is 3. The description adds meaning by clarifying that 'query' matches against ID as well as name, which the schema does not mention, and by explaining the returned flow_refs format. This extra context raises the score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('Search'), the resource ('BIS SDMX dataflow registry'), and the matching criteria ('by keyword against dataflow name and ID'). It also distinguishes the tool by showing how results connect to fetch_dataset, making it distinct from generic search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: use this when you need to find dataflows by keyword and obtain flow_refs for fetch_dataset. It does not explicitly list alternatives or exclusions, but the integration hint ('ready to pass to fetch_dataset') gives practical usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation3/5

Most tools have carefully written distinctions, but several overlap in purpose: ask_pipeworx versus ask_pipeworx_beta are currently functionally identical, and ask_pipeworx, deep_research, validate_claim, and the Polymarket research tools all sit on the same factual-question axis. The long descriptions help an agent choose, but the set still has multiple ambiguous boundaries.

Naming Consistency3/5

Names are uniformly snake_case and mostly readable, with clear prefix families like pipeworx_*, polymarket_*, and ask_pipeworx*. However, the verb-noun pattern is inconsistent: many tools are noun phrases (entity_profile, recent_alerts, polymarket_edges) and some are bare verbs (remember, recall, forget), so the naming is not predictable across the full set.

Tool Count2/5

35 tools is well above the 25-tool threshold and feels like an organic platform dump rather than a curated server. The broad data-platform scope partly justifies the number, but the presence of near-duplicate entry points and one-off utilities (generate_llms_txt, ai_visibility_check, scan_dependency) makes the set feel bloated rather than cohesive.

Completeness4/5

For a read-heavy data/research platform the surface is unusually complete: discovery, single-lookup, grounded-answer, deep-research, entity resolution, comparison, change-tracking, subscriptions, memory, and feedback are all covered. Missing write/execution capabilities like placing trades or modifying BIS flows are reasonable absences for this kind of server; the main gap is a dedicated historical/trend utility beyond the general router.

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