Skip to main content
Glama

tokenpull_submit

Idempotent

Pulls local token usage from session logs and submits it to SigRank board for scoring. Computes cascade per window and publishes under a codename, or previews locally without one.

Instructions

Pull your LOCAL token usage from session logs AND publish it to the SigRank board in one call — the zero-paste flow. Reads the four canonical pillars (input, output, cacheCreate, cacheRead) per window from your local logs, computes the cascade, and submits each window to the board where it is re-scored server-side and tagged with the source platform. Requires a codename to publish; omit for a local preview only. Token-only — no prompt content is read or transmitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNoSubmit only this time window (default: all 4 windows). Use "7d" for recent activity or "all" for all-time ranking.
codenameNoOperator codename to publish under on the leaderboard (e.g. "Iron Lotus"). Required to submit — omit for local preview only.
platformNoSource platform to pull from (default: claude). Supported: amp, kimi, qwen, pi, openclaw, droid, codebuff, gemini, copilot, opencode, goose, kilo, hermes, devin, other, omp, proxy, claude, codex, grok, multi. 'multi' = combined cascade summed across all locally-detected platforms (needs 2+ active). 'devin' reads from ~/.local/share/devin/cli/sessions.db (SQLite, all windows). 'other' reads from a user-supplied JSON file (set SIGRANK_OTHER_PATH). Each platform reads its own session logs locally.

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
Behavior4/5

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

The description adds meaningful behavioral context beyond the annotations: it reads four canonical pillars, computes a cascade, submits each window, re-scores server-side, and tags with source platform. It also discloses that no prompt content is read or transmitted, which is valuable privacy-relevant information. The behavior does not contradict annotations.

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 sentences, front-loaded with the core action and use case. Every sentence adds value: purpose, mechanics, publish/preview condition, and privacy guarantee. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the end-to-end flow, privacy constraints, and the publish-vs-preview distinction. Because an output schema exists, return-value details are not required. It could mention the default 'all 4 windows' behavior explicitly, but the schema and context signals already provide that.

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 the schema already documents all three parameters and their enums. The description adds little beyond the schema: it reiterates the codename requirement and adds the 'token-only' privacy note, but does not enrich the meaning of 'window' or 'platform' beyond what the schema provides.

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 clear compound action: pull local token usage from session logs and publish it to SigRank in one call. It distinguishes itself as the 'zero-paste flow' versus sibling tools like tokenpull, submit_paste, and rank_paste by specifying this combined behavior.

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?

It gives clear conditional guidance: require a codename to publish, omit it for a local preview only. It does not explicitly name alternatives or exclusions, but the 'one call' framing and preview-or-publish condition make the primary use case clear.

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