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resolve_entity

FIRST STOP for a name/entity question: 'what is X', 'when did X come out / launch', 'is X legit / a rug', 'who is X', or a bare proper noun / ticker. Runs the crypto + on-chain routes IN PARALLEL (NFT collection, DEX token across chains, listed coin, and on-chain if it's an address) and returns EVERY real match at once, labeled, so you never miss the crypto entity by guessing one route. fingers' users are crypto/agents: a bare name usually means the token/NFT/project, not a same-named YouTube channel or film. Call this BEFORE concluding from web search alone; lead with the match that has real traction (holders / volume / liquidity / market cap), then add any non-crypto namesake from web_search as secondary. For a launch date, take the contract it returns and confirm the deploy date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesthe name, ticker, or 0x address to resolve, e.g. 'normies', 'serc', '0x9eb6...'

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden. It discloses that it runs crypto and on-chain routes in parallel, returns every real match at once, and clarifies that bare names in this context should be interpreted as crypto entities. However, it does not detail return format or potential errors, but the key behavioral traits are covered.

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 dense but purposeful, with each sentence contributing to usage or interpretation. It is front-loaded with the 'FIRST STOP' positioning and examples. A minor typo ('fingers' users) slightly detracts, but the structure is effective.

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?

For a single-parameter tool with no output schema, the description is quite complete: it covers question types, behavior, interpretation, and edge-case handling (launch date). It doesn't define the exact return structure, but the description's guidance on leading with traction match implies the return includes confidence/metadata.

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 description already fully covers the query parameter with examples (name, ticker, address). The description reinforces this by mentioning bare proper nouns and address handling, but adds no new syntax or format details, so a baseline of 3 is appropriate given 100% schema coverage.

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 explicitly states it is the FIRST STOP for name/entity questions, lists specific example queries (what is X, when did X launch, is X a rug), and differentiates from siblings by emphasizing parallel multi-route resolution returning every match. This clearly distinguishes it from individual lookup tools like dex_token or nft_info.

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?

Provides explicit when-to-use guidance: 'FIRST STOP' for entity questions, 'Call this BEFORE concluding from web search alone', and gives direction on how to use results (lead with traction match, add non-crypto namesakes from web_search as secondary). Also gives specific handling for launch date queries.

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

A3.7/5.0
Disambiguation3/5

Many tools share the same core purpose (safety checks) differentiated mainly by chain or asset type, and descriptions are detailed enough to distinguish them most of the time. However, pairs like rug_check/rugcheck and deployer_check/deployer_reputation have overlapping purposes that could lead to misselection.

Naming Consistency3/5

Most tool names follow a lowercase snake_case pattern with clear descriptors, but there are notable exceptions like 'rugcheck', 'defillama', and 'verify'. Additionally, the 'rug_check' vs 'rugcheck' pair is an obvious naming inconsistency.

Tool Count2/5

44 tools is excessive for any server. Even for a broad security/due-diligence purpose, many tools (especially the robinhood_* series) are highly specialized and could be consolidated into fewer actions.

Completeness4/5

The tool surface is impressively comprehensive, covering token/NFT safety, transaction simulation, whale/address tracking, stock analysis, prediction markets, and project validation. Minor gaps exist (e.g., no ENS resolution or stock price history), but they are not critical for the server's core purpose.

Resources