Skip to main content
Glama

SigRank — AI Operator Benchmarking

Who Operates Like Me — Nearest Neighbor Finder

who_operates_like_me
Read-onlyIdempotent

Finds operators whose operating signature most resembles yours. Takes 4 pillars or a codename, computes your signature, then searches the live leaderboard for the nearest neighbors by signature distance. Returns: nearest operators, similarity %, where they outperform you, where you outperform them, and what separates you from the better operator. Makes the leaderboard feel like a network, not a list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputNo
limitNoNumber of nearest neighbors to return (default 5)
outputNo
windowNo30d
codenameNoOperator codename (alternative to pillars)
cache_readNo
cache_writeNo

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral detail: it computes a signature, searches a live leaderboard, and returns specific comparative fields, including where the operator outperforms you and vice versa. No contradiction with annotations.

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?

The description is front-loaded with the core purpose and then compactly explains the process and return values. Every sentence contributes something except the final stylistic sentence, which is not harmful but adds little operational value.

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

Completeness3/5

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

With no output schema, the explicit list of returned information is helpful. However, the description is incomplete for a 7-parameter tool: it does not clarify how to supply the 4 pillars, what the cache parameters mean, or how the optional parameters interact with defaults. It covers the primary use case but leaves too many operational details unresolved.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 29%, so the description must compensate, but it merely says '4 pillars or a codename' without mapping '4 pillars' to the input parameter or explaining how the numeric input should encode them. The codename and limit are documented in the schema, but output, cache_read, and cache_write remain undefined in both schema and description. The lack of required parameters also creates ambiguity about whether pillars are optional or default to the current operator.

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 specific outcome—finds operators whose operating signature most resembles yours—and explains the mechanism: compute signature from 4 pillars or a codename and search the live leaderboard by signature distance. It clearly differentiates from siblings like get_leaderboard by describing the result as a network rather than a list.

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

Usage Guidelines3/5

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

The description implies when to use the tool—when you want similar operators and a comparative breakdown—but it never explicitly says when not to use it or which alternative to choose. There is no routing guidance such as 'use get_leaderboard for raw lists' or 'use operator_signature for just a signature.'

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation2/5

Several tools have significantly overlapping purposes: benchmark_me and compare_to_field both compare a user's cascade to the field, operator_signature and who_operates_like_me both find comparable operators, and rank_paste/rank_windows overlap as metric calculators. Descriptions clarify the output format, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names are lowercase snake_case, which is readable, but the pattern is mixed: get_leaderboard and simulate_change are verb-first, field_anomaly and operator_gap are noun-first, and rank_if, rank_paste, benchmark_me, and who_operates_like_me break the convention entirely. There is a loose semantic system, but no strong predictable verb_noun pattern.

Tool Count3/5

16 tools sits right at the heavy borderline, and the count feels inflated by overlapping tools that could be consolidated. The domain is broad enough to justify more than a handful of tools, but the duplication makes the set feel heavier than its actual functional surface.

Completeness4/5

The tool set covers the core benchmarking workflow well: reading leaderboard/operator data, computing metrics, comparing to the field, diagnosing weaknesses, simulating changes, and suggesting improvements. There are minor gaps, such as no way to retrieve a user's raw token pillars from a codename for simulation tools that require four pillars, but these are workable.