dexscreener-mcp

Server Quality Checklist

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Latest release: v1.0.2
Disambiguation4/5
Most tools target distinct data retrieval patterns: search, address lookups, pair details, and discovery lists. However, search_pairs and get_tokens both return pair data, which could cause confusion when an agent has a contract address.
Naming Consistency4/5
Predominantly verb_noun (search_pairs, get_token_pools, get_tokens, get_pair), but latest_token_profiles and top_boosted_tokens deviate from the verb pattern, making the set slightly inconsistent.
Tool Count5/5
Six tools cover the core DexScreener functionality without redundancy; the count is appropriate for a focused data provider.
Completeness4/5
The set covers search, direct address lookups, pair details, and discovery features. Missing historical/OHLC data but that's beyond the typical scope for first-level data access.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral context. It only states 'Get details' without specifying the return structure, error behavior, or any side effects. For a simple read this is minimal, but it lacks sufficient detail given no output schema.
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?
A single sentence with clear, front-loaded information. Every word earns its place, with no unnecessary details or repetition.
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?
Given the low complexity (2 required params, no output schema), the description is adequate for a direct lookup but does not describe the response shape or usage edge cases. It is minimally viable but not rich enough to fully guide an agent without additional context.
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 coverage is 100%, and the description restates the parameters without adding new meaning. The schema already explains 'Pair / pool address' and chain id examples, so the description adds no semantic value beyond the structured fields.
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 uses a specific verb ('get') and a clear resource ('specific pair'), and explicitly identifies the lookup keys (chain id and pair address). This distinguishes it from siblings like search_pairs (search-based) and get_token_pools (by token).
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 usage when the exact chainId and pairId are known, but it does not mention alternatives or exclusions. There is no guidance on when to use search_pairs instead, leaving the agent to infer from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full responsibility for behavioral disclosure. It mentions 'currently' and 'top-boosted' but does not explain ordering, pagination, data format, or any assumed read-only nature. The agent is left without critical behavioral context.
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?
The description is a single, front-loaded sentence with no redundant words. It earns a perfect score for conciseness and structure.
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?
For a tool with no parameters and no output schema, the description provides the essential purpose but omits details about return shape, ordering, or limits. It is minimally viable but leaves gaps, especially since no output schema exists to clarify the response.
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?
The tool has zero parameters, so the input schema is trivially complete. Per rubric, with 0 params the baseline is 4; the description adds no parameter-specific meaning but also does not need to.
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 the action ('Get') and resource ('top-boosted tokens on DexScreener'), with specific qualifiers ('currently', 'most promoted / trending'). This distinguishes it from sibling tools like latest_token_profiles by emphasizing trending/promoted status rather than recency.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as search_pairs or latest_token_profiles. There is no mention of use cases, exclusions, or complementary tools, leaving the agent to infer selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It communicates a read-only operation via 'Get' and scopes results to a specific token and chain, but it does not disclose potential limitations such as supported chain formats, pagination, data volume, or the structure of each returned pool. This leaves some behavioral ambiguity.
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?
The description is a single, concise sentence that is front-loaded with the core action and object. There is no extraneous information, making it highly scannable and efficient.
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?
This is a simple read-only tool with two well-documented parameters and no output schema. The description sufficiently conveys what the tool does and the scope, but it does not elaborate on the format or fields of the returned pool objects. Given the simplicity and clear inputs, it is mostly complete but could benefit from a bit more detail on return structure.
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 coverage is 100% for both parameters, with chainId and tokenAddress already well-described. The description only adds generic references to 'specific token' and 'given chain', providing no new semantic detail beyond the schema. Thus, the baseline of 3 applies.
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 uses the verb 'Get' with a specific resource ('all liquidity pools (pairs) for a specific token on a given chain'). It distinguishes itself from sibling 'get_pair' by emphasizing 'all' pools, indicating it returns a collection rather than a single pair.
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 usage when one needs all pools for a specific token and chain, but it does not explicitly mention when to use this over alternatives like get_pair or search_pairs. No exclusions or alternative guidance are provided, so guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It does disclose that the tool returns price, 24h change, volume, liquidity, and FDV, which is useful behavioral context. However, it omits any mention of pagination, result limits, data freshness, or potential edge cases (e.g., exact-match constraints). This is adequate but not thorough for a tool with zero annotation support.
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?
The description is two sentences, front-loaded with the primary action, and includes only essential information. Every word contributes: the search scope ('across all chains') and the return fields are useful and concise. There is no filler or redundancy.
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 read-only search tool, the description covers the purpose, input scope, and typical return data. It does not explain whether the result is a list or a single object, nor mention any response limits, which could be a minor gap. Overall, it is sufficient for straightforward invocation, but a bit more context on result shape would make it fully complete.
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?
The input schema already fully describes the 'query' parameter with examples ('PEPE', 'WIF', contract address), giving 100% coverage. The tool description repeats the same semantics without adding new details or nuances beyond the schema. Baseline 3 applies because schema coverage is high and the description does not need to compensate.
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 the verb ('Search'), the resource ('DexScreener trading pairs'), and the scope ('by token name, symbol, or contract address across all chains'). It differentiates from sibling tools like get_pair (exact pair lookup) and get_tokens (token lookup) by emphasizing broad search, and it also lists the returned data fields.
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?
The description provides clear context for when to use this tool: when you need to find trading pairs via partial identifiers across all chains. However, it does not explicitly mention alternatives like get_pair or get_tokens, nor does it state when not to use it. It stops short of explicit exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It adds the constraint of up to 30 addresses and comma-separated format, which is valuable. However, it does not disclose potential errors, output shape, or whether only existing tokens are returned, limiting transparency.
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?
A single sentence that is concise and front-loaded with the core purpose, followed by a parenthetical constraint. No wasted words.
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 tool is simple with only two required parameters and no output schema. The description covers the core function and input constraints, but lacks details on return format or behavior for invalid addresses, which would enhance completeness.
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?
The schema already describes both parameters clearly, so the baseline is 3. The description adds the batch limit ('up to 30'), which is not in the schema, providing extra semantic 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?
The description uses a specific verb 'Get' and clearly identifies the resource as 'pair/market data' for token addresses, including a batch size limit. It distinguishes from siblings like get_pair (which likely handles a single pair) by supporting multiple addresses.
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?
The description implies usage when needing market data for one or more token addresses, and the comma-separated format up to 30 indicates batch usage. However, it does not explicitly mention alternatives or when not to use this tool, leaving some ambiguity relative to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the transparency burden. It conveys that the tool returns recent token profiles but omits behavioral details such as ordering, pagination, time windows, or potential duplicates. The description is not misleading but provides minimal insight beyond the basic purpose.
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?
The description is a single sentence that front-loads the verb 'Get' and immediately states the resource. It contains no filler or redundant information, making it highly concise and scannable.
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?
Given the tool's simplicity (no parameters, no output schema), the description adequately conveys what it does. It could be more explicit about the return format or that it returns a collection of profiles, but for a basic fetch tool the context is sufficient.
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?
The tool has zero parameters, and the input schema is empty with 100% coverage by definition. Per the rubric, 0 params earns a baseline of 4, and the description does not need to explain parameter semantics.
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 the tool fetches 'latest token profiles freshly listed/updated on DexScreener' with the intent of finding 'new tokens to watch.' This specific verb+resource combination distinguishes it from sibling tools like 'top_boosted_tokens' or 'search_pairs'.
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?
The description provides a clear use case ('new tokens to watch') and implies it is for fresh listings/updates. However, it does not explicitly mention when not to use it or name alternative tools for different scenarios, such as searching for specific pairs or getting top boosted tokens.
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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