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Blackwood State

by io.github.Blackwood-State
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Summarize text

blackwood_text_summarize
Read-onlyIdempotent

Use when you need to summarize text such as an article, notes or a transcript into a short summary. Do not use for uRLs or PDFs (extract the text first), exact quotes or citations. Returns JSON with a concise summary of the plain text you send (up to 20,000 characters): one short paragraph and key bullet points, written by IBM Granite 4.0 Micro on Workers AI for 0.008 USDC per call. Typical response time 8000 ms (p95). Category: Inference.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesPlain text to summarize, e.g. an article or a transcript (80 to 20,000 characters)
languageNoLanguage for the summary, e.g. English or Portuguese (default: the language of the text)
maxBulletsNoMost bullet points to return (1-10, default 5)
response_formatNoconcise (the default when omitted) keeps the JSON short: long fields such as base64 images or audio come back as separate image/audio content blocks and other long strings are cut with a marker; detailed returns the full JSON

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / anyOf
      Previous value: -[
-  {
-    "additionalProperties": false,
-    "properties": {
-      "bullets": {
-        "items": {
-          "type": "string"
-        },
-        "type": "array"
-      },
-      "inputChars": {
-        "type": "number"
-      },
-      "model": {
-        "type": "string"
-      },
-      "summary": {
-        "type": "string"
-      },
-      "usage": {
-        "additionalProperties": false,
-        "properties": {
-          "completionTokens": {
-            "type": [
-              "number",
-              "null"
-            ]
-          },
-          "promptTokens": {
-            "type": [
-              "number",
-              "null"
-            ]
-          }
-        },
-        "required": [
-          "promptTokens",
-          "completionTokens"
-        ],
-        "type": "object"
-      }
-    },
-    "required": [
-      "summary",
-      "bullets",
-      "model",
-      "inputChars",
-      "usage"
-    ],
-    "type": "object"
-  },
-  {
-    "description": "x402 PaymentRequired (returned with isError when payment is needed)",
-    "properties": {
-      "accepts": {
-        "items": {
-          "type": "object"
-        },
-        "type": "array"
-      },
-      "error": {
-        "type": "string"
-      },
-      "extensions": {
-        "type": "object"
-      },
-      "resource": {
-        "type": "object"
-      },
-      "x402Version": {
-        "type": "integer"
-      }
-    },
-    "required": [
-      "x402Version",
-      "accepts"
-    ],
-    "type": "object"
-  }
-]New value: +[
+  {
+    "additionalProperties": false,
+    "properties": {
+      "bullets": {
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "inputChars": {
+        "type": "number"
+      },
+      "model": {
+        "type": "string"
+      },
+      "next": {
+        "additionalProperties": false,
+        "description": "Suggested next paid call in the same family (call it in a loop when the task continues)",
+        "properties": {
+          "method": {
+            "enum": [
+              "GET",
+              "POST"
+            ],
+            "type": "string"
+          },
+          "sample": {
+            "description": "Free fixed sample of the whole family",
+            "type": "string"
+          },
+          "service": {
+            "description": "Service id of the suggested next paid call",
+            "type": "string"
+          },
+          "tool": {
+            "description": "MCP tool name of the same call",
+            "type": "string"
+          },
+          "url": {
+            "description": "Paid route of the next call",
+            "type": "string"
+          },
+          "useWhen": {
+            "description": "When the next call is the right one",
+            "type": "string"
+          }
+        },
+        "required": [
+          "service",
+          "tool",
+          "method",
+          "url",
+          "useWhen",
+          "sample"
+        ],
+        "type": "object"
+      },
+      "summary": {
+        "type": "string"
+      },
+      "usage": {
+        "additionalProperties": false,
+        "properties": {
+          "completionTokens": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "promptTokens": {
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "promptTokens",
+          "completionTokens"
+        ],
+        "type": "object"
+      }
+    },
+    "required": [
+      "summary",
+      "bullets",
+      "model",
+      "inputChars",
+      "usage"
+    ],
+    "type": "object"
+  },
+  {
+    "description": "x402 PaymentRequired (returned with isError when payment is needed)",
+    "properties": {
+      "accepts": {
+        "items": {
+          "type": "object"
+        },
+        "type": "array"
+      },
+      "error": {
+        "type": "string"
+      },
+      "extensions": {
+        "type": "object"
+      },
+      "resource": {
+        "type": "object"
+      },
+      "x402Version": {
+        "type": "integer"
+      }
+    },
+    "required": [
+      "x402Version",
+      "accepts"
+    ],
+    "type": "object"
+  }
+]
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds real operational context the annotations cannot: the underlying model (IBM Granite 4.0 Micro on Workers AI), a per-call price of 0.008 USDC and p95 latency of 8000 ms. It does not discuss failure modes (e.g. what happens with input below 80 characters or rate limiting), so it stops short of a 5.

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

Conciseness4/5

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

Front-loaded with the trigger condition and dense with useful facts (exclusions, output shape, model, cost, latency, category) with no filler. It is nonetheless one sprawling sentence chain with slightly awkward casing ('uRLs'), and the cost/latency details could be split out for readability.

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?

An output schema exists so return values need not be explained, annotations cover safety, schema covers parameters, and the description supplies cost, latency and model provenance. An agent has everything needed to decide and call 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%, so text, language, maxBullets and response_format are all documented in the schema, including defaults and ranges. The description restates the 20,000-character ceiling and the paragraph-plus-bullets output but adds no syntax or format detail beyond the schema, which is the expected baseline-3 case.

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?

States a specific verb and resource ('summarize text such as an article, notes or a transcript into a short summary') and immediately delimits scope by excluding URLs/PDFs, quotes and citations, which routes the agent to blackwood_webpage_markdown / blackwood_pdf_extract without opening their schemas.

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?

Gives an explicit when-to-use clause plus concrete exclusions with a remedy ('extract the text first'), and rules out quote/citation tasks that a sibling such as a search tool would serve. Nothing about applicability is left to inference.

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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