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tracetify-mcp

by io.github.tracetify

Who competes for the same keywords

research_competitors

The domains fighting a target for the same search terms, with shared-keyword count, their keyword totals and average rank. Use when the user asks who their competitors are, or who a company is up against. Each result says whether we already have a full growth report for that domain — read those with read_report for free instead of tracing them again. Costs credits; cached results are free, and a domain already looked up via research_domain_overview is free here too.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to find competitors for, e.g. notion.so

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only show readOnlyHint: false and destructiveHint: false, so the description carries the burden. It discloses credit costs, free cached results, and that a domain already looked up via research_domain_overview is free here, adding meaningful operational behavior beyond the structured fields.

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, each earning its place: what the tool returns, when to use it and the report alternative, then cost behavior. No filler or redundancy.

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?

For a single-parameter tool with no output schema, the description covers the return fields, usage scenario, alternative path via read_report, and cost/caching behavior. This is enough for an agent to select and invoke it correctly.

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% and already describes the domain parameter. The description adds targeted meaning by referring to the 'target' domain and clarifying that using a domain already looked up via research_domain_overview makes the call free, which is useful semantic context for the parameter value.

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?

Description opens with a concrete output definition: domains competing for the same search terms with shared-keyword count, keyword totals, and average rank. It also states exactly when to use it ('when the user asks who their competitors are') and references the sibling read_report, which differentiates it from related research tools.

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 says 'Use when the user asks who their competitors are, or who a company is up against.' It also provides an alternative for existing growth reports: 'read those with read_report for free instead of tracing them again,' plus cost/caching conditions that guide when it is cheaper to call.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: backlink_directories vs research_backlinks differ (directories vs actual backlinks), gsc_* tools cover different views of own data (overview/pages/queries), research_* tools each address a separate question (competitors, domain overview, AI mentions, backlinks), and the trace/audit/report lifecycle tools have clear roles (start, poll, read, unlock). No two tools appear to do the same thing.

Naming Consistency4/5

There is a clear pattern: verb_noun for actions (get_trace, read_report, start_trace, unlock_report, search_reports), research_* prefix for external lookups, gsc_* prefix for own Search Console data, and site_audit_* for audits. Minor inconsistency: backlink_directories doesn't follow the verb_noun style (no verb), but overall the naming is predictable and grouped logically.

Tool Count4/5

At 15 tools, this is at the upper end of the well-scoped range (3-15). Each tool serves a distinct function within the SEO/growth analysis domain, so the count feels justified rather than bloated. However, it's slightly heavy, which is why it misses a 5.

Completeness4/5

The tool surface covers the full lifecycle of growth reports (search, read, start trace, poll, unlock), ownsite GSC data (overview, pages, queries), competitor research (domain, backlinks, brand, competitors), and site audits (start/get). Minor gaps include no way to delete or manage reports beyond reading, and no direct keyword research beyond GSC queries, but the core workflows are complete.

Resources