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Google_search_console

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

TDQS

A5/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), description reveals embedding model (BGE-base-en), chunking (500-char windows), similarity metric (cosine), and input cap (200K chars with truncation flag).

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?

Four dense, front-loaded sentences with no filler. Every sentence adds value, from purpose to technical details to usage guidance.

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?

Despite no output schema, description explains return format (top-N passages with offsets and similarity scores), making behavior fully predictable.

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

Parameters5/5

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

Schema has 100% coverage. Description adds examples for query parameter, details text truncation behavior, and specifies limit range, enhancing understanding beyond the schema.

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 'Semantic search INSIDE a fetched record' with specific verb and resource. It distinguishes from sibling tools like ask_pipeworx_grounded, which operates on the whole document.

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 record is too big to cram into the prompt' and mentions pairing with ask_pipeworx_grounded, providing clear context for when and how to use.

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

A3.5/5.0
Disambiguation2/5

The set has several overlapping clusters: ask_pipeworx and ask_pipeworx_beta are explicitly identical, multiple polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) occupy the same prediction-market space, and ai_visibility_check/scan_competitor_ai_presence are near-duplicates. A few tools (memory triad, gsc_* calls) are crisp, but the boundaries between the meta-research tools (ask_pipeworx, deep_research, discover_tools, validate_claim) are not obvious enough to prevent misselection.

Naming Consistency2/5

Naming is a mixed bag: some tools follow snake_case verb_noun (gsc_list_sites, resolve_entity, search_within), others are lowercased concatenations (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and several are bare nouns or adjectives (recent_alerts, forget, recall, process). There is no consistent verb style or separator convention across the set.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them (gsc_list_sites, gsc_list_sitemaps, gsc_inspect_url, gsc_search_analytics) relate to the server's stated Google Search Console purpose. The remaining 31 are a sprawling Pipeworx data/prediction-market/memory toolkit, making the count wildly disproportionate to the apparent scope.

Completeness1/5

For a Google Search Console server, the surface is severely incomplete: it can list sites/sitemaps, inspect URLs, and query analytics, but lacks sitemap submission, property add/remove, URL removal/access control, and other core GSC operations. Conversely, the 31 off-domain tools make the domain itself incoherent — an agent cannot tell what this server is actually for.

Resources