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bls-labor-mcp-server

by io.github.cyanheads
OverviewDiscussionsAdmin

List BLS Surveys

bls_list_surveys
Read-onlyIdempotent

List BLS survey programs with their abbreviation codes, full names, and metadata about calculation support and annual averages. Use to discover which survey covers a topic before calling bls_search_series; bls_search_series covers only the surveys in its offline index, and series in the others are fetched by SeriesID with bls_get_series. Optional category filter narrows results to prices, employment, wages, productivity, injuries, or time_use surveys.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoOptional category filter. One of: prices, employment, wages, productivity, injuries, time_use. A survey can appear under more than one category (CES under employment and wages). Omit to list all surveys.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
totalNoTotal surveys returned.
surveysNoBLS survey programs matching the filter, sorted alphabetically by abbreviation.
categoryFilterNoCategory filter applied, if any. Absent when all surveys were listed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / category / description
      Previous value: -"Optional category filter. One of: prices, employment, wages, productivity, injuries, time_use. Omit to list all surveys."New value: +"Optional category filter. One of: prices, employment, wages, productivity, injuries, time_use. A survey can appear under more than one category (CES under employment and wages). Omit to list all surveys."
  2. Changed6 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / additionalProperties
      Added value: +false
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / anyOf
      Added value: +[
+  {
+    "not": {
+      "required": [
+        "error"
+      ]
+    },
+    "required": [
+      "surveys",
+      "total"
+    ]
+  },
+  {
+    "required": [
+      "error"
+    ]
+  }
+]
    • addedOutput schema / properties / error
      Added value: +{
+  "additionalProperties": {},
+  "description": "Present when the call failed. Absent on success.",
+  "properties": {
+    "code": {
+      "description": "JSON-RPC error code for this failure.",
+      "maximum": 9007199254740991,
+      "minimum": -9007199254740991,
+      "type": "integer"
+    },
+    "data": {
+      "additionalProperties": {},
+      "properties": {
+        "reason": {
+          "description": "Machine-readable failure mode. Declared by this tool: `invalid_api_key`: BLS rejected the configured BLS_API_KEY as invalid. `service_unavailable`: BLS /surveys API is unreachable or returns a non-200 response. `serialization_failure`: BLS /surveys response cannot be parsed (malformed JSON or unexpected schema). Other values are possible when a failure originates below the handler.",
+          "examples": [
+            "invalid_api_key",
+            "service_unavailable",
+            "serialization_failure"
+          ],
+          "type": "string"
+        },
+        "recovery": {
+          "additionalProperties": {},
+          "description": "Actionable next step for the caller.",
+          "properties": {
+            "hint": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "hint"
+          ],
+          "type": "object"
+        },
+        "retryable": {
+          "description": "Whether retrying may succeed.",
+          "type": "boolean"
+        }
+      },
+      "type": "object"
+    },
+    "message": {
+      "description": "Human-readable description of what went wrong.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "code",
+    "message"
+  ],
+  "type": "object"
+}
    • removedOutput schema / required
      Removed value: -[
-  "surveys",
-  "total"
-]
  3. Changed1 schema field changed
    • changedOutput schema / properties / surveys / items / properties / hasAnnualAverages / description
      Previous value: -"True when the survey publishes annual average observations."New value: +"True when BLS reports that the survey publishes annual average observations. Advisory only: it does not predict whether a given series returns annual-average rows for bls_get_series with annual_average=true — LN, CE, LA and SM report true yet return none. Read annualAverageRows on that response for what actually came back."
  4. Changed1 schema field changed
    • addedOutput schema / properties / categoryFilter
      Added value: +{
+  "description": "Category filter applied, if any. Absent when all surveys were listed.",
+  "type": "string"
+}
  5. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already carry readOnlyHint=true and idempotentHint=true, so the description's job is lighter. It adds useful context about the nature of the data (metadata about calculation support and annual averages) and clarifies that the tool is a discovery step with optional filtering. No extra behaviors are disclosed, but given the annotations, this is adequate.

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?

The description is two sentences with zero redundancy. The core purpose is front-loaded, the usage guidance is embedded, and the category filter is mentioned last. Every sentence earns its place with no filler.

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?

For a simple list/discovery tool with one optional parameter and an existing output schema, the description covers purpose, usage context, and relationship to siblings. Nothing critical is missing—an agent can safely call it without needing additional information.

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

Parameters3/5

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

Schema description coverage is 100%; the category parameter is already fully explained in the schema (enum values, that a survey can appear under multiple categories). The description merely repeats the enum list, adding no new meaning. Baseline 3 is appropriate because the schema does the heavy lifting.

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 states a specific verb ('List') and resource ('BLS survey programs') with details about what is included (abbreviation codes, full names, metadata). It distinguishes itself from siblings by explicitly contrasting coverage with bls_search_series and bls_get_series, so an agent can tell exactly which tool fits.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance: 'Use to discover which survey covers a topic before calling bls_search_series.' It also explains the limitation of bls_search_series (offline index) and the alternative for other series (bls_get_series), making the decision path unambiguous.

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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