secedgar-mcp-server

Secedgar Dataframe Describe

secedgar_dataframe_describe

Read-onlyIdempotent

List the dataframes (df_XXXXX_XXXXX) registered by the data-returning secedgar_* tools — any tool whose response carries a dataset handle stages its full result set here. Each entry surfaces source tool, query parameters, creation/expiry timestamps, row count, column schema, and whether the dataframe is truncated relative to the upstream source. Read the column schema here before writing SQL for secedgar_dataframe_query.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No	Optional table name (df_XXXXX_XXXXX) to describe a single dataframe. Omit to list all dataframes.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No	Present when the call failed. Absent on success.
`dataframes`	No	Active dataframes for this tenant, newest first. Empty when none are registered.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 22, 2026

changedOutput schema / properties / error / properties / data / properties / reason / description

Previous value: -"Machine-readable failure mode. Declared by this tool: `canvas_unavailable`: The DataCanvas service is not configured for this deployment Other values are possible when a failure originates below the handler."New value: +"Machine-readable failure mode. Declared by this tool: `canvas_unavailable`: The DataCanvas service is not configured for this deployment. Other values are possible when a failure originates below the handler."

Changed6 schema fields changedAug 22, 2026

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": [
+      "dataframes"
+    ]
+  },
+  {
+    "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: `canvas_unavailable`: The DataCanvas service is not configured for this deployment Other values are possible when a failure originates below the handler.",
+          "examples": [
+            "canvas_unavailable"
+          ],
+          "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: -[
-  "dataframes"
-]
```

AddedMay 17, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already mark it read-only and idempotent, and the description adds a useful behavioral contract: this is a staging/catalog view of dataframes produced by data-returning tools, including expiry timestamps and truncation status. It explains what 'describe' covers without repeating annotation facts.

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?

Three tightly worded sentences: the first scopes the resource, the second enumerates returned fields, and the third gives actionable guidance. Every sentence carries information 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?

With a full output schema and read-only/idempotent annotations, the description covers all necessary usage context: what dataframes are, what fields are surfaced, and how to use it before querying. No critical gap affects an agent's ability to invoke it correctly.

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 coverage is 100%, and the `name` parameter is already documented as optional and as a filter. The description reinforces that omitting it lists all dataframes but adds no new parameter-level semantics beyond the schema.

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?

Clearly identifies the resource (registered dataframes) and the action (list/describe), and specifies the df_XXXXX_XXXXX naming pattern. It also distinguishes itself from secedgar_dataframe_query by indicating this is the metadata catalog rather than the SQL execution tool.

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?

Explicitly directs the agent to read the column schema here before writing SQL against secedgar_dataframe_query, tying the tool to a concrete workflow. It does not enumerate when alternatives should be preferred, but for a catalog tool this is sufficient.

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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