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Glama

Lichess

by io.github.pipeworx-io

Tv Channels

tv_channels
Read-onlyIdempotent

Currently-featured TV games per variant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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: +[
+  {}
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": {
+    "properties": {
+      "fen": {
+        "description": "Current position FEN",
+        "type": "string"
+      },
+      "id": {
+        "description": "Game ID",
+        "type": "string"
+      },
+      "lastMove": {
+        "description": "Last move in UCI notation",
+        "type": "string"
+      },
+      "players": {
+        "description": "Players in the game",
+        "items": {
+          "type": "object"
+        },
+        "type": "array"
+      }
+    },
+    "type": "object"
+  },
+  "description": "Featured TV games per variant",
+  "type": "object"
+}
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide strong safety guarantees (readOnlyHint, idempotentHint, destructiveHint false). The description adds 'currently-featured' and 'per variant,' which gives context about the data's nature but does not disclose additional behavioral traits like update frequency or authorization needs.

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 a single sentence that is concise, front-loaded, and contains no filler. Every word adds value, making it efficient for an agent to parse.

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?

Given the tool has no parameters, rich annotations, and an output schema (as indicated by context signals), the brief description is sufficient to inform the agent about the tool's purpose and return type. No additional details are needed.

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?

The tool has zero parameters, so the schema is fully covered (100%). The description adds meaning by explaining the output scope ('currently-featured TV games per variant'), which helps the agent understand what the tool returns without needing parameter guidance.

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 the tool returns 'currently-featured TV games per variant,' which is a specific verb-resource combination. While sibling tools cover diverse areas like betting or research, none overlap directly, so differentiation is inherent.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs. alternatives or when not to use it. The description simply describes the output, leaving the agent to infer usage context without further direction.

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

All Lichess toolsInspect
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TDQS

B3.3/5.0
Disambiguation2/5

The server mixes chess tools with numerous data query tools from Pipeworx, causing significant overlap. Multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research have similar purposes, making it difficult for an agent to choose correctly. Chess tools are distinct but compete with many unrelated tools.

Naming Consistency2/5

Tool names follow no consistent pattern: chess tools use mostly underscores (top_players, opening_explorer), Pipeworx tools use mixed styles (ask_pipeworx, deep_research, entity_profile), and memory/subscription tools use simple verbs (remember, subscribe). The naming is inconsistent across the set.

Tool Count2/5

With 41 tools, the count is high and unfocused. A chess server would typically have 10-15 tools; the remaining 31 tools from Pipeworx are unrelated and overwhelm the set. The server tries to cover too many domains, making it bloated for its primary purpose.

Completeness2/5

The chess-specific tools (10) cover basic queries but lack deeper chess analysis (e.g., puzzles, board evaluation). The extensive Pipeworx tools are out of scope for a Lichess server, resulting in an incomplete surface for the expected domain and an excessive surface for unrelated data lookups.

Resources