Skip to main content
Glama

submit_paste

Idempotent

Submit token counts to rank your AI coding efficiency and publish the result to the live SigRank leaderboard. Get an instant local score preview, then the server re-scores and assigns your rank under a codename.

Instructions

Ranks a paste of token counts AND publishes it to the live SigRank board at signalaf.com in one call. First computes the cascade locally for an instant preview (yield, leverage, velocity, class, card), then submits the raw paste to the board's web-paste endpoint, which re-parses and re-scores it server-side. The server score is authoritative — it may differ from the local preview if the board applies additional validation. Returns both the local preview and the server response (including the operator's new rank if accepted). A codename is required to publish — omit it for a local preview only (no board submission). Token-only, no auth required. Use this when you have token counts from ccusage or a dashboard and want to both see your score and publish it. Do NOT use this if you want to pull your local usage automatically — use tokenpull_submit for the zero-paste flow. Do NOT use this for multi-window dashboard pastes — use rank_windows to rank them first, then submit each window. After calling this, use get_operator with your codename to verify your submission appeared on the board.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesToken counts to rank and submit. Two formats: (1) JSON {"input":N,"output":N,"cacheCreate":N,"cacheRead":N} from ccusage (preferred — the board parses this reliably), or (2) four whitespace-separated numbers: input output cacheCreate cacheRead. The 4-number form ranks locally but the board may reject it. Example: {"input":1000000,"output":500000,"cacheCreate":50000,"cacheRead":800000}
codenameNoOperator codename to publish under on the leaderboard (e.g. "Ghost Falcon"). Required to submit — omit for local preview only (no board submission, just returns the local cascade result). Must be a non-empty string.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoError or skip reason if status is not ok
statusNoSubmission status
previewNo
server_responseNoServer-side response including new rank if accepted
Behavior5/5

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

The description discloses the two-phase behavior (local preview vs server authoritative score), the return payload (both local preview and server response), the absence of auth, and the requirement for a codename to publish. This goes well beyond annotations, which only declare idempotence and non-destructiveness.

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 detailed but every sentence adds value—purpose, workflow, return value, usage, exclusions, and post-submission verification. It is front-loaded with the primary action and logically structured, earning its length.

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 is present so detailed return types are unnecessary, but the description states the key return elements (local preview and server response). It covers prerequisites, auth, alternatives, and post-submission follow-up (get_operator), making it complete for a complex tool.

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 coverage is 100%, setting a baseline of 3. However, the description adds meaning beyond the schema: it explains the two accepted text formats, warns that the 4-number form may be rejected by the board, and provides a concrete example. This elevates it above the baseline.

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 opens with a clear verb+resource: 'Ranks a paste of token counts AND publishes it to the live SigRank board.' It also distinguishes from siblings by explicitly contrasting with tokenpull_submit and rank_windows, making the purpose unambiguous.

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?

Explicitly states when to use ('Use this when you have token counts from ccusage or a dashboard and want to both see your score and publish it') and when not to use, naming alternatives (tokenpull_submit for zero-paste, rank_windows for multi-window). This fully covers usage context and exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/SunrisesIllNeverSee/sigrank-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server