loki-tail-mcp

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose. The two query tools (instant vs range) are explicitly differentiated in their descriptions, and the discovery/analysis tools (labels, patterns, volume, fields, containers) do not overlap. Even tail_container is clearly a convenience wrapper for a specific use case.
Naming Consistency4/5
Six of nine tools follow a consistent loki_verb_noun pattern (query_instant, query_range, tail_container, list_labels, list_label_values, list_containers). Three tools (patterns, log_volume, detected_fields) use noun phrases, breaking the verb-first convention. This is a minor inconsistency that doesn't hinder usability.
Tool Count5/5
Nine tools is a well-scoped number for a Loki exploration server. Each tool serves a distinct purpose without redundancy, covering querying, metadata discovery, log analysis, and container navigation. The count is neither too small nor overwhelming.
Completeness5/5
The tool set covers the full range of Loki interactions: instant queries, range queries, metadata discovery (labels, values), container listing and tailing, pattern mining, volume ranking, and field detection for parsing. There are no obvious gaps for a read-only log exploration workflow.
Average 4.4/5 across 9 of 9 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on August 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose that results are limited to a lookback window and that values are 'seen' in that window, which adds context. However, it omits any detail about pagination, limits, ordering, or behavior when the label has no values. This is adequate but not rich.
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 extremely concise: one purpose line followed by compact parameter documentation. Every sentence adds value, and the most important information is front-loaded. No filler 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 params, no nested objects) and the presence of an output schema, the description covers the essential invocation details. However, it lacks usage context, alternative-tool differentiation, and behavioral caveats, making it only moderately complete for agent decision-making.
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 coverage is 0%, but the description fully compensates by explaining both parameters. It gives concrete examples for label ('container', 'service_name', etc.) and lookback ('1h', '24h', '7d') with the default value. This is more informative than the bare schema properties.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the operation: 'List all values seen for a given label in the lookback window.' It identifies the resource (label values) and the scope (lookback). It does not explicitly differentiate from sibling tools like loki_list_labels, but the wording is sufficiently specific to avoid major confusion.
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?
There is no guidance on when to use this tool versus alternatives such as loki_list_labels, loki_query_range, or loki_patterns. The description does not mention exclusions, prerequisites, or typical use cases beyond the literal action.
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 carries the full burden. It discloses the instant, single-point-in-time behavior but does not mention return format, side effects, or permissions. The read-only nature is implied by 'query' but not explicitly stated, and the existing output schema mitigates missing return value details.
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 three sentences, each adding value: the first defines the core action, the second gives a metric-query use case, and the third points to an alternative tool. No fluff or repetition, and the most important information is front-loaded.
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?
The description covers purpose, an example, and alternative guidance, but with only 3 parameters and zero schema descriptions, it leaves the agent without enough detail on parameter semantics. The output schema covers return values, so that is not a gap, but parameter documentation remains thin.
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?
The input schema has zero description coverage for parameters. The description provides an example query for the `query` parameter but does not explain the `time` (e.g., format, default 'now') or `limit` (e.g., max results) parameters. Given the low schema coverage, the description should have compensated more thoroughly.
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 ('Run') and resource ('LogQL instant query') with a clear temporal scope ('at a single point in time'). It also distinguishes from the sibling tool `loki_query_range` by explicitly recommending the latter for raw log search.
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?
The description provides explicit guidance on when to use this tool: 'Best for metric queries' with a concrete LogQL example. It also names the alternative for raw logs ('prefer `loki_query_range`'), covering both use and non-use cases.
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, the description must carry the behavioral transparency burden. It does disclose useful behavioral aspects: the tool samples a time window (minutes), returns fields with metadata (name, type, cardinality, parser), and is meant for pre-parse discovery. However, it does not explicitly state that it is a read-only operation or mention potential limitations (e.g., empty results, performance effects). This is adequate but not rich.
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 compact and well-structured. It opens with a one-sentence purpose, followed by a valuable usage example, then a succinct parameter list. Every sentence adds information without redundancy. The parameter formatting uses clear key: description pairs, making it easy to scan.
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 (3 params, output schema present, no annotations), the description is nearly complete. It covers what the tool does, when to use it, how the output feeds into LogQL queries, and explains each parameter. Minor gaps include not stating behavior when no fields are found or prerequisites like the container must have recent logs, but these are not critical for basic use.
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 description coverage is 0%, so the description compensates well. Each parameter gets a meaningful explanation: 'container: exact container name', 'minutes: window to sample', 'limit: max fields'. It also clarifies defaults (60, 20). The example further demonstrates the container parameter's use in a query. This goes beyond the schema's bare titles and defaults.
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's function: 'Discover the fields Loki can auto-extract from a container's logs.' The verb 'Discover' and the specific resource (auto-extracted fields) distinguish it from sibling tools like loki_query_range or loki_list_labels. It also explains the output (name, type, cardinality, parser) and how it relates to creating parse stages.
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 explicit usage context: 'Use this before writing a LogQL parse stage in loki_query_range.' It includes a concrete example showing how to use the discovered fields in a query. However, it does not explicitly mention when not to use it or name alternative tools, though the sibling context and the 'fields' vs 'labels' distinction implicitly guide selection.
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 exist, so the description carries the burden. It implies a read-only listing operation and discloses performance behavior ('Shorter is faster'), but it does not mention potential result size, auth needs, or any side effects. This is adequate but not rich.
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?
Two sentences: the first states the tool's purpose, the second explains the parameter. No filler, front-loaded information, and every sentence earns its place.
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 simple single-parameter listing tool with an output schema present, the description covers the essential behavior and parameter semantics. The sibling distinction is clear from the purpose statement, and no missing prerequisites or caveats are critical.
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?
The input schema only defines 'lookback' as a string with a default, giving no semantic detail. The description compensates fully by specifying valid examples ('1h', '24h', '7d'), the default, and the performance tradeoff. This adds significant meaning 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 opens with 'List all label names Loki has seen', which is a specific verb+resource statement. It clearly distinguishes this from the sibling tool loki_list_label_values, which lists values for a given label. The purpose is unambiguous.
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 by stating the lookback window and the default ('Default 1h'), plus a practical performance hint ('Shorter is faster'). It does not explicitly mention alternatives or when not to use, but the usage context is clear enough for straightforward selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the core behavior (ranking by bytes), the time window, the row limit, and the container scoping behavior. It doesn't mention anything contradictory, though it omits details like read-only nature or aggregation semantics, which are likely implied for a ranker.
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 extremely concise, front-loaded with the primary action, and includes a compact parameter dictionary. Every sentence adds value, and there is no redundant filler.
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 simple tool with 3 optional parameters and an output schema, the description covers all necessary aspects: what it does, how to use parameters, and the use case. Nothing essential is missing.
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 descriptions are absent (0% coverage), but the description fully compensates by explaining all three parameters: container scope, minutes as the measurement window, and limit as max rows, including defaults. This adds meaning beyond the raw 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 the tool's purpose: 'Rank containers by log volume (bytes)' and provides a concrete use case ('which service suddenly got noisy'). It distinguishes itself from sibling tools like query/range or tail by focusing on volume ranking.
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 gives clear guidance on when to use the tool and how to adjust scope: 'Leave container empty to rank the whole fleet; set it to scope to one container.' It doesn't explicitly mention alternatives or when not to use, but the use case is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses defaults for start/end/direction, limit clamping, and the return format for both log and metric queries. It doesn't mention auth or rate limits, but these are not critical for a query tool's behavior.
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 well-structured, with each parameter on its own line and a separate return format explanation. It is concise yet complete, with no redundant or filler content.
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?
The tool has 5 parameters and no annotations, but the description covers all parameters, defaults, output formats, and provides query examples. It is complete enough for an agent to select and invoke the tool correctly without further clarification.
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 description coverage is 0%, so the description fully compensates by explaining each parameter: query syntax, start/end formats (duration, RFC3339, ns epoch), limit handling, and direction semantics with defaults. This adds substantial meaning beyond the bare 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 states 'Run a LogQL range query against Loki' with concrete examples, clearly distinguishing this range query tool from the instant query sibling. The verb and resource are specific and unambiguous.
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 gives clear context for when to use this tool (querying over a time range) and explains behaviors like defaults and return formats. It doesn't explicitly name alternatives or exclusions, but the range query nature and parameter details imply appropriate usage versus instant queries or tailing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It discloses that partial container names are resolved against live containers, that a unique match is automatically tailed and flagged, and that ambiguous matches return candidates. It also notes filter case-sensitivity and application of LogQL, which are important behavioral details.
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 compact and uses a clear per-parameter layout for scannability. Each sentence provides necessary information—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?
Given the presence of an output schema, the description need not explain return values. It covers parameter semantics, resolution behavior, and filter mechanics, making the tool fully operational from the description alone. The behavioral disclosures about candidate returns and flagging round out the context.
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 description coverage is 0%, but the description documents each parameter meaningfully: container name resolution with examples, minutes default, limit default, and filter semantics (LogQL substring, case-sensitive). This adds substantial value 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 opens with a clear verb ('Tail') and resource ('most recent log lines for a single container'), immediately distinguishing it from sibling Loki query tools. The tool's scope is explicitly limited to a single container, which separates it from list and query tools.
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 explains the intended use case for tailing logs and provides essential context for the container parameter (exact names vs. fuzzy resolution). It does not explicitly mention when to use an alternative like loki_query_range, but the usage context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It honestly reveals that the tool filters out ephemeral names and explains why, plus notes that filtered items are still accessible via other tools. It does not mention error conditions or auth requirements, but for a simple list operation, the filtering behavior is the key nuance and is well covered.
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 well-structured with a clear main sentence, a brief explanatory note about filtering, and a concise parameter definition. Every sentence adds value without redundancy, and it remains compact while covering key details.
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?
Given the tool's simplicity (one optional parameter) and the presence of an output schema, the description is complete. It covers the purpose, filtering behavior, lookback format, and alternative tools. No critical information for invocation or interpretation is missing.
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?
The schema provides only a `lookback` string with a default and no description. The description compensates fully by defining the parameter as a duration, providing example formats (`1h`, `24h`, `7d`), and restating the default. This gives the agent all necessary syntax and semantic meaning.
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's action ('List') and specific resource ('durable container names actively logging in the lookback window'). It also distinguishes itself from siblings by mentioning that ephemeral names are filtered and pointing to `loki_list_label_values` for the raw unfiltered list, making the purpose unambiguous.
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?
The description explicitly explains when to use this tool (for durable container names) and provides clear alternatives: ephemeral names remain queryable via `loki_query_range`, and raw unfiltered lists are available via `loki_list_label_values`. This gives the agent clear decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the prerequisite of the Loki pattern ingester, the caveat that only logs ingested after its enablement are mined, and the output format (ranked templates with occurrence counts). This is excellent transparency beyond what annotations would provide.
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 appropriately sized, front-loaded with the primary purpose, and each sentence adds value. The parameter explanations are integrated compactly without unnecessary fluff.
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?
Given an output schema exists, the description does not need to detail return values. It covers the use case, prerequisites, parameter meanings, and output format, making it complete for a 2-parameter tool.
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 description coverage is 0%, so the description must explain parameters. It does: 'container: exact container name (use loki_list_containers / loki_tail_container to find it)' and 'minutes: how far back to mine. Default 60.' This fully compensates for the lack of schema descriptions.
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 it 'Mine a container's recent logs into recurring patterns', using a specific verb and resource, and differentiates itself from sibling tools by summarizing noisy logs into templates. This is unique among the sibling tools.
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?
It describes the use case as 'the fastest way to summarize what a noisy service is saying' and tells users how to find the container name using sibling tools. However, it does not explicitly mention when not to use this tool (e.g., for raw log queries) versus alternatives, so it lacks explicit exclusionary guidance.
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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